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This DSPA Appendix introduces strategies to utilize soft qualitative data, implement qualitative data analytics, and develop mixed data methods. Specifically, this appendix offers a comprehensive technical overview of cutting-edge qualitative and mixed methods research techniques in healthcare. We present the mathematical foundations, offer algorithmic specifications, and demonstrate complete methodological frameworks for modern approaches including AI-powered sentiment analysis, mobile ethnography, video-reflexive ethnography, participatory co-design, and advanced mixed methods designs. We show examples demonstrating practical implementation in biomedical, nursing, and clinical research contexts.
The Nurse AI Trainer (NAIT) offers interactive examples, data, and hands-on training in qualitative and mixed data analytics, from the NAIT Modules, select NAIT Module 7.
Qualitative research in healthcare has evolved from simple thematic analysis to sophisticated mixed-method approaches incorporating artificial intelligence, real-time data collection, and participatory frameworks. This evolution addresses the increasing complexity of healthcare systems and the need for patient-centered evidence.
Qualitative data can be quantified using information-theoretic measures. The entropy \(H(X)\) of a qualitative dataset \(X\) with \(n\) categories is:
\[H(X) = -\sum_{i=1}^n p(x_i) \log_2 p(x_i)\]
where \(p(x_i)\) is the probability of category \(i\) occurring in the dataset.
Consider a dataset of 100 patient interviews coded into 4 themes with frequencies \([40, 30, 20, 10]\). In this case, the corresponding data-driven estimates of the probabilities are: \(p(x_1) = 0.4\), \(p(x_2) = 0.3\), \(p(x_3) = 0.2\), \(p(x_4) = 0.1\), and the entropy is:
\[H(X) = -(0.4\log_2(0.4) + 0.3 \log_2(0.3) + 0.2 \log_2(0.2) + 0.1 \log_2(0.1))\]
\[H(X) = -(0.4 \times (-1.32) + 0.3 \times (-1.74) + 0.2 \times (-2.32) + 0.1 \times (-3.32)) = 1.85 \text{ bits}\]
The mathematical foundation for semantic analysis uses Singular Value Decomposition (SVD):
\[A = U\Sigma V^T\]
where: - \(A\) is the term-document matrix (\(m \times n\)) - \(U\) is the left singular vectors (\(m \times r\)) - \(\Sigma\) is the (square) diagonal matrix of singular values (\(r \times r\)) - \(V^T\) is the right singular vectors (\(r \times n\)) - \(r\) is the rank of the reduced space
In qualitative network analysis, relationships are represented as graphs \(G = (V, E)\) where \(V\) = set of vertices (actors, concepts, themes) and \(E\) = set of edges (relationships, co-occurrences).
For each vertex \(v \in V\), centrality measures include:
More modern sentiment analysis employs transformer architectures with attention mechanisms:
\[\text{Attention}(Q,K,V) = \text{softmax}\left(\frac{QK^T}{\sqrt{d_k}}\right)V\]
where: - \(Q\) = queries matrix - \(K\) = keys matrix - \(V\) = values matrix - \(d_k\) = dimension of key vectors
In BERT-based sentiment classification, the probability of sentiment class \(c\) given (observed) text \(x\) is:
\[P(c|x) = \text{softmax}(W_c h_{[CLS]} + b_c)\]
where \(h_{[CLS]}\) is the BERT representation of the \([CLS]\) token, and \(W_c\) and \(b_c\) are learned parameters for class \(c\).
Below is a skeleton of a Python implementation protocol.
class SentimentClassifier(nn.Module):
def __init__(self, n_classes=3):
super().__init__()
self.bert = BertModel.from_pretrained('bert-base-uncased')
self.dropout = nn.Dropout(0.3)
self.classifier = nn.Linear(768, n_classes)
def forward(self, input_ids, attention_mask):
outputs = self.bert(input_ids=input_ids,
attention_mask=attention_mask)
pooled_output = outputs.pooler_output
output = self.dropout(pooled_output)
return self.classifier(output)Set the following parameters: - Learning rate = \(2 \times 10^{-5}\) - Batch size = 16 - Epochs = 4 - Loss function = CrossEntropyLoss() - Optimizer = AdamW with weight decay = 0.01
Dataset: 500 patient feedback comments from ICU discharge surveys
Sample Data:
| Comment ID | Text | Manual Label |
|---|---|---|
| 1 | “The nurses were incredibly caring and attentive” | Positive |
| 2 | “I felt anxious about the noise levels at night” | Negative |
| 3 | “The doctor explained everything clearly” | Positive |
| 4 | “Wait times for pain medication were too long” | Negative |
Results:
Confusion Matrix:
Predicted
Actual Pos Neg Neu
Positive 184 12 4
Negative 15 167 18
Neutral 8 19 173Mobile ethnography employs Experience Sampling Method (ESM) with temporal dynamics:
\[Y_{it} = \beta_0 + \beta_1 X_{it} + \beta_2 T_{it} + \beta_3(X_{it} \times T_{it}) + u_i + \varepsilon_{it}\]
where: - \(Y_{it}\) = outcome for person \(i\) at time \(t\) - \(X_{it}\) = predictor variables - \(T_{it}\) = time-varying factors - \(u_i\) = person-specific random effect - \(\varepsilon_{it}\) = residual error
Consider the following data collection protocol.
Appropriate statistical analyses may include:
Multilevel Modeling:
Level 1 (Within-person): \[Y_{ti} = \pi_{0i} + \pi_{1i}(\text{TIME}_{ti}) + \pi_{2i}(\text{CONTEXT}_{ti}) + \varepsilon_{it}\]
Level 2 (Between-person): \[\pi_{0i} = \beta_{00} + \beta_{01}(\text{PERSON}_i) + r_{0i}\] \[\pi_{1i} = \beta_{10} + \beta_{11}(\text{PERSON}_i) + r_{1i}\]
Intraclass Correlation Coefficient (ICC): \[\text{ICC} = \frac{\sigma^2_{\text{between}}}{\sigma^2_{\text{between}} + \sigma^2_{\text{within}}}\]
Study Design:
Sample Mobile Entry:
{
"participant_id": "P_047",
"timestamp": "2024-03-15T14:30:00Z",
"prompt_type": "medication_reminder",
"responses": {
"took_medication": "yes",
"difficulty_level": 2,
"side_effects": "none",
"mood": 6,
"context": "at_work",
"free_text": "Remembered because of calendar alert"
},
"location": {
"lat": 42.3601,
"lng": -71.0589,
"accuracy": 5.0
}
}Statistical Results:
Fixed Effects: - Intercept (\(\beta_{00}\)): 5.23 (SE = 0.18, p < 0.001) - Time slope (\(\beta_{10}\)): -0.05 (SE = 0.02, p = 0.012) - Context [home vs work] (\(\beta_{01}\)): 0.34 (SE = 0.15, p = 0.024)
Random Effects: - Intercept variance (\(\sigma^2_{u0}\)): 1.23 - Slope variance (\(\sigma^2_{u1}\)): 0.08 - Residual variance (\(\sigma^2_e\)): 0.94
\(\text{ICC} = \frac{1.23}{1.23 + 0.94} = 0.57\)
Key Findings: - Medication adherence declined slightly over time (\(\beta = -0.05\)) - Home context associated with better adherence than work (\(\beta = 0.34\)) - High between-person variability (\(\text{ICC} = 0.57\))
VRE combines three analytical lenses:
Consider the following Video Analysis Protocol:
| Time | Speaker | Verbal Content | Non-verbal | Context |
|---|---|---|---|---|
| 00:15 | Nurse A | “So we have Mr. Johnson…” | Points to chart | Handover at bedside |
| 00:18 | Nurse B | “Okay, what’s his status?” | Nods | Receiving information |
Adjacency Pair Structure: - First Pair Part (FPP): Question/Request - Second Pair Part (SPP): Answer/Compliance - Preferred/Dispreferred response analysis
Next, consider the following reflexive session protocol:
Structure:
Facilitation Techniques:
Setting: Cardiac surgery operating room
Participants: 2 surgeons, 2 nurses, 1 anesthesiologist,
1 perfusionist
Video Duration: 45 minutes (pre-bypass phase)
Selected Clips: 3 clips × 2-3 minutes each
Transcript:
01 SURG1: Can I have a fifteen blade please?
02 (0.8)
03 NURSE1: ((reaches toward instrument table))
04 SURG1: →Fifteen blade?
05 NURSE1: Oh sorry ((hands over blade))
06 SURG1: Thank youAnalysis:
Reflexive Session Insights:
Quantitative Measures: - Average response time to instrument requests: 2.3 seconds - Repeated requests: 12% of all requests - Successful first-attempt retrievals: 88%
Participatory co-design employs principles from:
For instance, assume the following Stakeholder Analysis Matrix:
| Stakeholder | Influence | Interest | Participation Level |
|---|---|---|---|
| Primary Users | High | High | Co-designer |
| Caregivers | Medium | High | Collaborator |
| Clinicians | High | Medium | Advisor |
| Administrators | High | Low | Informant |
| IT Staff | Medium | Medium | Technical Partner |
The co-design process model may involve:
Activities:
Deliverables:
Activities:
Deliverables:
Activities:
Deliverables:
A quantitative model evaluation framework may utilize the following metrics:
Quantitative Metrics:
\[\text{Usability Score} = \frac{\sum w_i \times s_i}{\sum w_i}\]
where \(w_i\) = weight for criterion \(i\) and \(s_i\) = score for criterion \(i\) (1-10 scale).
Criteria weights:
Qualitative Assessment:
Context: Mobile app for elderly patients with multiple chronic conditions.
Participants:
User Personas:
Persona 1: “Tech-Anxious Margaret” - Age: 74 - Conditions: Diabetes, hypertension, arthritis - Tech comfort: Low (2/10) - Primary concerns: Making mistakes, complex interfaces - Key quote: “I just want something simple that won’t confuse me”
Persona 2: “Organized Robert” - Age: 68 - Conditions: Heart disease, COPD - Tech comfort: Medium (6/10) - Primary concerns: Integration with existing systems - Key quote: “I need this to work with my doctor’s records”
Journey Map Key Insights:
Generated Concepts (\(n=23\)):
Concept Evaluation Scores:
| Concept | Feasibility | Desirability | Viability | Total |
|---|---|---|---|---|
| Voice Assistant | 7.2 | 8.4 | 6.8 | 7.5 |
| Photo Identification | 8.1 | 7.9 | 8.2 | 8.1 |
| Smart Dispenser | 6.5 | 8.8 | 5.9 | 7.1 |
| Adherence Tracking | 9.2 | 6.4 | 8.9 | 8.2 |
Prototype Features:
User Testing Results (n=12 patients, 3 rounds):
Round 1:
Round 2:
Round 3:
Final Evaluation:
Mathematical Framework:
QUAL → quan
Priority: QUAL > quan
Integration: Results → InterpretationStatistical Power Calculation:
\[n = \frac{(Z_{1-\frac{\alpha}{2}} + Z_{1-\beta})^2 \times (\sigma_1^2 + \sigma_2^2)}{(\mu_1 - \mu_2)^2}\]
where: - \(\alpha\) = Type I error rate (0.05) - \(\beta\) = Type II error rate (0.20) - \(\sigma\) = Standard deviation - \(\mu\) = Population mean
Integration Model:
QUAN + qual
Priority: QUAN > qual
Integration: Data → Analysis → InterpretationMeta-inference Quality Index:
\[\text{MQI} = \frac{\text{Credibility} \times \text{Transferability} \times \text{Dependability} \times \text{Confirmability}}{4}\]
where each component is rated on a 1-5 scale.
Consider the following transformative framework.
Social Justice Integration: Participatory + Transformative + Mixed Methods
Evaluation Criteria:
Research Question: How do structural barriers and patient experiences interact to create disparities in diabetes care?
Design: Concurrent Embedded (QUAN + qual)
Sample: N = 2,847 diabetes patients from 15
clinics
Design: Cross-sectional survey
Variables: - Outcome: HbA1c levels (continuous) -
Predictors: Insurance type, clinic location, SES indicators -
Covariates: Age, gender, diabetes duration, comorbidities
Statistical Model:
\[\text{HbA1c}_{ij} = \beta_0 + \beta_1(\text{Insurance}_{ij}) + \beta_2(\text{SES}_{ij}) + \beta_3(\text{Location}_{ij}) + \beta_4(\text{Age}_{ij}) + \beta_5(\text{Duration}_{ij}) + u_j + e_{ij}\]
Where: - \(i\) = individual patient - \(j\) = clinic - \(u_j\) = clinic-level random effect - \(e_{ij}\) = individual-level residual
Results:
Fixed Effects: - Intercept: 8.12 (SE = 0.23, p < 0.001) - Insurance [uninsured vs insured]: 0.67 (SE = 0.15, p < 0.001) - SES [low vs high]: 0.43 (SE = 0.12, p < 0.001) - Rural location: 0.29 (SE = 0.18, p = 0.108)
Random Effects: - Clinic variance: 0.34 - Residual variance: 1.87 - \(\text{ICC} = \frac{0.34}{0.34 + 1.87} = 0.15\)
Sample: n = 48 patients (purposive sampling from
quantitative sample)
Method: Semi-structured interviews
Duration: 45-90 minutes
Analysis: Thematic analysis using Braun & Clarke
framework
Sample Interview Guide:
Qualitative Findings:
Theme 1: “Navigating the System” (100% of participants) - Subtheme 1a: Insurance barriers and coverage gaps - Subtheme 1b: Complex referral processes - Subtheme 1c: Medication access challenges
Theme 2: “Quality of Patient-Provider Relationships” (87% of participants) - Subtheme 2a: Communication barriers - Subtheme 2b: Cultural competence issues - Subtheme 2c: Time constraints in visits
Theme 3: “Community and Social Support” (73% of participants) - Subtheme 3a: Family support systems - Subtheme 3b: Peer networks and diabetes groups - Subtheme 3c: Community resource availability
Joint Display:
| Quantitative Results | Qualitative Findings | Meta-Inference |
|---|---|---|
| Insurance effect (\(\beta=0.67\)) | Insurance barriers theme | CONVERGENT |
| SES effect (\(\beta=0.43\)) | Navigation challenges | CONVERGENT |
| Rural effect (\(\beta=0.29\), ns) | Community support varies | DIVERGENT |
| Clinic variance (\(ICC=0.15\)) | Provider relationship quality | EXPANSION |
Mixed Methods Inference:
Transformative Impact:
Lincoln & Guba Framework:
Credibility (Internal Validity):
\[\text{Credibility Index} = \frac{\text{Member Checking} + \text{Peer Debriefing} + \text{Triangulation} + \text{Prolonged Engagement}}{4}\]
Each component scored 0-1 based on quality criteria.
Transferability (External Validity): - Thick description provision - Purposive sampling strategy - Context specification - Demographic reporting
Dependability (Reliability):
Inter-rater Reliability:
\[\kappa = \frac{P_o - P_e}{1 - P_e}\]
Where: - \(P_o\) = observed agreement - \(P_e\) = expected agreement by chance
Confirmability (Objectivity): - Audit trail maintenance - Reflexivity documentation - Researcher positionality statements - Data-conclusion linkage verification
Inference Quality Assessment:
\[\text{Design Quality} = \frac{\sum(w_i \times q_i)}{\sum w_i}\]
Components: - Design appropriateness (w=0.25) - Implementation rigor (w=0.20) - Integration effectiveness (w=0.30) - Meta-inference legitimacy (w=0.25)
Each component rated 1-5 scale.
Automated Coding Validation:
def calculate_coding_reliability(human_codes, ai_codes):
"""
Calculate inter-rater reliability between human and AI coding
"""
from sklearn.metrics import cohen_kappa_score
# Align coding segments
aligned_human, aligned_ai = align_codes(human_codes, ai_codes)
# Calculate Cohen's Kappa
kappa = cohen_kappa_score(aligned_human, aligned_ai)
# Calculate percentage agreement
agreement = sum(h == a for h, a in zip(aligned_human, aligned_ai)) / len(aligned_human)
return {
'kappa': kappa,
'agreement': agreement,
'interpretation': interpret_kappa(kappa)
}Bias Detection Algorithms:
def detect_sampling_bias(sample_demographics, population_demographics):
"""
Statistical test for sampling bias
"""
from scipy.stats import chisquare
# Chi-square goodness of fit test
chi2, p_value = chisquare(sample_demographics, population_demographics)
# Effect size (Cramér's V)
n = sum(sample_demographics)
cramers_v = np.sqrt(chi2 / (n * (len(sample_demographics) - 1)))
return {
'chi2': chi2,
'p_value': p_value,
'cramers_v': cramers_v,
'bias_detected': p_value < 0.05
}Core Infrastructure:
Platform: Python 3.9+
Required Libraries:
- pandas >= 1.3.0
- numpy >= 1.21.0
- scikit-learn >= 1.0.0
- torch >= 1.9.0
- transformers >= 4.11.0
- nltk >= 3.6.0
- spacy >= 3.4.0
- networkx >= 2.6.0
- matplotlib >= 3.4.0
- seaborn >= 0.11.0
- plotly >= 5.3.0
- streamlit >= 1.2.0 (for web interface)Database Schema:
CREATE TABLE participants (
participant_id VARCHAR(50) PRIMARY KEY,
demographics JSON,
consent_date TIMESTAMP,
study_arm VARCHAR(20)
);
CREATE TABLE qualitative_data (
data_id VARCHAR(50) PRIMARY KEY,
participant_id VARCHAR(50),
data_type ENUM('interview', 'observation', 'diary', 'photo'),
content TEXT,
metadata JSON,
timestamp TIMESTAMP,
FOREIGN KEY (participant_id) REFERENCES participants(participant_id)
);
CREATE TABLE codes (
code_id VARCHAR(50) PRIMARY KEY,
data_id VARCHAR(50),
code_text VARCHAR(200),
start_position INT,
end_position INT,
coder_id VARCHAR(50),
coding_date TIMESTAMP,
FOREIGN KEY (data_id) REFERENCES qualitative_data(data_id)
);IRB Requirements Checklist:
Data Protection Framework:
Encryption: AES-256 (data at rest), TLS 1.3 (data in transit)
Access Control: Role-based with multi-factor authentication
Audit Logging: All data access logged with 7-year retention
Anonymization: k-anonymity (k≥5) with l-diversity
Backup Strategy: 3-2-1 rule with geographic distributionResearcher Competency Matrix:
| Skill Domain | Novice | Intermediate | Advanced | Expert |
|---|---|---|---|---|
| Qualitative Theory | 20h | 40h | 80h | 120h |
| Interview Techniques | 15h | 30h | 50h | 80h |
| Coding and Analysis | 25h | 50h | 100h | 150h |
| Software Proficiency | 10h | 25h | 40h | 60h |
| Ethics and Compliance | 8h | 15h | 25h | 40h |
| Mixed Methods Integration | 12h | 30h | 60h | 100h |
Certification Requirements: - Human Subjects Research Training (CITI) - Qualitative Research Methods Certification - Data Security and Privacy Training - Cultural Competency Training - Software-Specific Certifications
Background: Phase III diabetes drug trial across 25 sites
Mixed Methods Integration: - Quantitative: Primary efficacy endpoints (HbA1c reduction) - Qualitative: Patient experience interviews (n=200) - AI Analysis: Sentiment analysis of patient diaries (n=1,200)
Implementation:
# Integrated analysis pipeline
class TrialAnalyzer:
def __init__(self):
self.efficacy_analyzer = EfficacyAnalyzer()
self.experience_analyzer = ExperienceAnalyzer()
self.sentiment_analyzer = SentimentAnalyzer()
def integrated_analysis(self, efficacy_data, interview_data, diary_data):
# Primary analysis
efficacy_results = self.efficacy_analyzer.analyze(efficacy_data)
# Qualitative analysis
experience_themes = self.experience_analyzer.code_interviews(interview_data)
# AI-powered sentiment analysis
sentiment_trends = self.sentiment_analyzer.analyze_diaries(diary_data)
# Integration analysis
integrated_results = self.integrate_findings(
efficacy_results, experience_themes, sentiment_trends
)
return integrated_results
def integrate_findings(self, efficacy, themes, sentiment):
"""
Integrate quantitative efficacy with qualitative insights
"""
# Correlation analysis between sentiment and efficacy
correlation_matrix = self.calculate_sentiment_efficacy_correlation(
efficacy, sentiment
)
# Theme-outcome mapping
theme_outcomes = self.map_themes_to_outcomes(themes, efficacy)
# Predictive modeling
combined_model = self.build_integrated_model(
efficacy, themes, sentiment
)
return {
'correlations': correlation_matrix,
'theme_outcomes': theme_outcomes,
'predictive_model': combined_model,
'recommendations': self.generate_recommendations()
}Results: - 23% improvement in patient retention - Identification of 3 previously unrecognized side effects - Site-specific intervention recommendations - Regulatory submission enhanced with patient voice data
Setting: 400-bed academic medical center ICU
VRE Implementation: - 120 hours of video data across 3 shifts - 45 nursing staff participants - 12 reflexive sessions - 6-month follow-up assessment
Quantitative Metrics:
Baseline vs. Post-Intervention: - Medication errors: 3.2/1000 → 1.8/1000 (44% reduction) - Communication delays: 12.4 min → 7.2 min (42% reduction) - Staff satisfaction: 6.2/10 → 8.1/10 (31% improvement) - Patient safety scores: 7.8/10 → 9.1/10 (17% improvement)
Qualitative Insights: - Standardized handoff protocols improved information transfer - Spatial reorganization reduced interruptions - Technology integration streamlined documentation - Team communication patterns became more inclusive
Context: Rural diabetes prevention program
Participatory Co-Design Process: - 8 community workshops (120 participants total) - 15 individual design sessions with high-risk individuals - 3 iterations of intervention prototyping - 6-month pilot implementation
Intervention Components (Co-Designed):
Outcome Evaluation:
Pre-Post Analysis (n=156):
Primary Outcomes: - HbA1c reduction: -0.8% (95% CI: -1.2, -0.4) - Weight loss: -5.2 kg (95% CI: -7.1, -3.3) - Physical activity increase: +78 min/week (95% CI: 45, 111)
Secondary Outcomes: - Self-efficacy scores: +1.4 points (95% CI: 0.9, 1.9) - Social support ratings: +2.1 points (95% CI: 1.6, 2.6) - Healthcare utilization: -23% emergency visits
Process Evaluation (Qualitative): - 94% found intervention culturally appropriate - 87% would recommend to family/friends - 78% continued participation at 6 months
Mathematical Framework:
For time-varying networks \(G(t) = (V, E(t))\), we analyze:
Dynamic Centrality:
\(C_{\text{dynamic}}(v,t) = \int_{t-\Delta t}^{t} C(v,\tau) \times w(t-\tau) d\tau\)
Where \(w(t-\tau)\) is a decay function weighting recent interactions more heavily.
Temporal Motif Analysis:
def analyze_temporal_motifs(network_sequence, motif_size=3, time_window=5):
"""
Identify recurring temporal patterns in qualitative networks
"""
motifs = {}
for t in range(len(network_sequence) - time_window):
# Extract temporal subgraph
subgraph = extract_temporal_subgraph(
network_sequence[t:t+time_window], motif_size
)
# Canonicalize motif representation
motif_signature = canonicalize_motif(subgraph)
# Count occurrences
motifs[motif_signature] = motifs.get(motif_signature, 0) + 1
# Statistical significance testing
significant_motifs = []
for motif, count in motifs.items():
p_value = calculate_motif_significance(motif, count, network_sequence)
if p_value < 0.05:
significant_motifs.append((motif, count, p_value))
return significant_motifsMathematical Model:
\(P(w|d) = \sum_z P(w|z) \times P(z|d)\)
Where: - \(w\) = word - \(d\) = document - \(z\) = topic (latent variable)
Gibbs Sampling Implementation:
def gibbs_sampling_lda(documents, K, alpha, beta, iterations=1000):
"""
Gibbs sampling for Latent Dirichlet Allocation
"""
# Initialize topic assignments randomly
topic_assignments = initialize_random_assignments(documents, K)
# Count matrices
doc_topic_counts = compute_doc_topic_counts(documents, topic_assignments, K)
topic_word_counts = compute_topic_word_counts(documents, topic_assignments, K)
for iteration in range(iterations):
for doc_id, document in enumerate(documents):
for word_pos, word in enumerate(document):
# Remove current assignment
old_topic = topic_assignments[doc_id][word_pos]
update_counts(doc_topic_counts, topic_word_counts,
doc_id, word, old_topic, -1)
# Sample new topic
topic_probs = compute_topic_probabilities(
doc_id, word, doc_topic_counts, topic_word_counts,
alpha, beta, K
)
new_topic = sample_topic(topic_probs)
# Update assignment and counts
topic_assignments[doc_id][word_pos] = new_topic
update_counts(doc_topic_counts, topic_word_counts,
doc_id, word, new_topic, 1)
if iteration % 100 == 0:
perplexity = calculate_perplexity(documents, doc_topic_counts,
topic_word_counts, alpha, beta)
print(f"Iteration {iteration}, Perplexity: {perplexity}")
return topic_assignments, doc_topic_counts, topic_word_countsExample Application:
# Patient interview analysis
documents = preprocess_interviews(interview_transcripts)
K = 8 # Number of topics
alpha = 0.1 # Document-topic concentration
beta = 0.01 # Topic-word concentration
topic_assignments, doc_topics, topic_words = gibbs_sampling_lda(
documents, K, alpha, beta, iterations=2000
)
# Extract top words for each topic
for topic_id in range(K):
top_words = get_top_words(topic_words[topic_id], vocabulary, n=10)
print(f"Topic {topic_id}: {', '.join(top_words)}")Model Specification:
Level 1 (Within-group): \(Y_{ij} = \beta_{0j} + \beta_{1j}(X_{1ij}) + \beta_{2j}(X_{2ij}) + r_{ij}\)
Level 2 (Between-group): \(\beta_{0j} = \gamma_{00} + \gamma_{01}(W_{1j}) + u_{0j}\) \(\beta_{1j} = \gamma_{10} + \gamma_{11}(W_{1j}) + u_{1j}\) \(\beta_{2j} = \gamma_{20} + \gamma_{21}(W_{1j}) + u_{2j}\)
Measurement Model: \(X_{1ij} = \lambda_{11}(\xi_{1ij}) + \delta_{1ij}\) \(X_{2ij} = \lambda_{21}(\xi_{1ij}) + \lambda_{22}(\xi_{2ij}) + \delta_{2ij}\)
Implementation in R:
library(lavaan)
library(semTools)
# Model specification
model <- '
# Level 1 (within)
level: 1
# Measurement model
Quality =~ Q1 + Q2 + Q3
Satisfaction =~ S1 + S2 + S3
# Structural model
Satisfaction ~ Quality + Experience
# Level 2 (between)
level: 2
# Measurement model
Quality =~ Q1 + Q2 + Q3
Satisfaction =~ S1 + S2 + S3
# Structural model
Satisfaction ~ Quality + Hospital_Type
'
# Fit model
fit <- sem(model, data = patient_data, cluster = "hospital_id")
# Model evaluation
summary(fit, fit.measures = TRUE, standardized = TRUE)
reliability(fit)Deep Learning for Narrative Analysis:
import torch
import torch.nn as nn
from transformers import AutoModel, AutoTokenizer
class NarrativeAnalyzer(nn.Module):
def __init__(self, model_name='bert-base-uncased', num_narrative_types=6):
super().__init__()
self.bert = AutoModel.from_pretrained(model_name)
self.dropout = nn.Dropout(0.3)
self.classifier = nn.Linear(768, num_narrative_types)
self.emotion_detector = nn.Linear(768, 8) # 8 basic emotions
self.temporal_lstm = nn.LSTM(768, 256, batch_first=True)
self.attention = nn.MultiheadAttention(768, 8)
def forward(self, input_ids, attention_mask):
# BERT encoding
outputs = self.bert(input_ids=input_ids,
attention_mask=attention_mask)
# Sequence representation
sequence_output = outputs.last_hidden_state
pooled_output = outputs.pooler_output
# Attention mechanism for key segments
attended_output, attention_weights = self.attention(
sequence_output, sequence_output, sequence_output
)
# Temporal dynamics
lstm_output, (hidden, cell) = self.temporal_lstm(attended_output)
# Classifications
narrative_type = self.classifier(self.dropout(pooled_output))
emotions = self.emotion_detector(self.dropout(pooled_output))
return {
'narrative_type': narrative_type,
'emotions': emotions,
'attention_weights': attention_weights,
'temporal_features': lstm_output
}
# Training loop
def train_narrative_analyzer(model, train_loader, val_loader, epochs=10):
optimizer = torch.optim.AdamW(model.parameters(), lr=2e-5)
criterion_narrative = nn.CrossEntropyLoss()
criterion_emotion = nn.BCEWithLogitsLoss()
for epoch in range(epochs):
model.train()
total_loss = 0
for batch in train_loader:
optimizer.zero_grad()
outputs = model(batch['input_ids'], batch['attention_mask'])
# Multi-task loss
narrative_loss = criterion_narrative(
outputs['narrative_type'], batch['narrative_labels']
)
emotion_loss = criterion_emotion(
outputs['emotions'], batch['emotion_labels']
)
total_loss = narrative_loss + 0.5 * emotion_loss
total_loss.backward()
optimizer.step()
# Validation
val_accuracy = evaluate_model(model, val_loader)
print(f"Epoch {epoch+1}, Loss: {total_loss:.4f}, Val Acc: {val_accuracy:.4f}")Version Control Protocol:
# Repository structure
qualitative-methods-study/
├── data/
│ ├── raw/ # Original data files
│ ├── processed/ # Cleaned data
│ └── anonymized/ # De-identified data
├── code/
│ ├── preprocessing/ # Data cleaning scripts
│ ├── analysis/ # Analysis scripts
│ ├── visualization/ # Plotting code
│ └── utils/ # Helper functions
├── results/
│ ├── figures/ # Generated plots
│ ├── tables/ # Statistical outputs
│ └── models/ # Trained models
├── docs/
│ ├── codebook.md # Variable descriptions
│ ├── analysis_plan.md # Pre-registered plan
│ └── methods.md # Detailed methods
├── environment.yml # Conda environment
├── requirements.txt # Python dependencies
└── README.md # Study overviewContainerization with Docker:
FROM python:3.9-slim
# Install system dependencies
RUN apt-get update && apt-get install -y \
gcc \
g++ \
git \
&& rm -rf /var/lib/apt/lists/*
# Set working directory
WORKDIR /app
# Copy requirements
COPY requirements.txt .
# Install Python dependencies
RUN pip install --no-cache-dir -r requirements.txt
# Copy application code
COPY . .
# Set environment variables
ENV PYTHONPATH=/app
ENV TRANSFORMERS_CACHE=/app/models
# Run analysis
CMD ["python", "main_analysis.py"]Anonymization Pipeline:
class QualitativeDataAnonymizer:
def __init__(self):
self.name_recognizer = spacy.load("en_core_web_sm")
self.date_pattern = re.compile(r'\d{1,2}/\d{1,2}/\d{4}|\d{4}-\d{2}-\d{2}')
self.phone_pattern = re.compile(r'\b\d{3}-\d{3}-\d{4}\b')
def anonymize_text(self, text, participant_id):
"""
Remove or replace identifying information
"""
# Named entity recognition
doc = self.name_recognizer(text)
anonymized_text = text
# Replace person names
for ent in doc.ents:
if ent.label_ == "PERSON":
anonymized_text = anonymized_text.replace(
ent.text, f"[PERSON_{hash(ent.text + participant_id) % 1000}]"
)
# Replace dates
anonymized_text = self.date_pattern.sub("[DATE]", anonymized_text)
# Replace phone numbers
anonymized_text = self.phone_pattern.sub("[PHONE]", anonymized_text)
# Replace specific locations
anonymized_text = self.replace_locations(anonymized_text)
return anonymized_text
def calculate_k_anonymity(self, dataset, quasi_identifiers):
"""
Verify k-anonymity requirements
"""
# Group by quasi-identifiers
groups = dataset.groupby(quasi_identifiers).size()
# Find minimum group size
k_value = groups.min()
return k_value, groups[groups == k_value].index.tolist()Study Registration Components:
study_metadata:
title: "Advanced Mixed Methods Analysis of Patient Experience"
investigators:
- name: "Dr. Jane Smith"
affiliation: "University Medical Center"
orcid: "0000-0000-0000-0000"
registration_date: "2024-03-15"
study_start_date: "2024-04-01"
expected_completion: "2024-10-31"
research_questions:
primary: "How do structural and interpersonal factors interact to influence patient satisfaction?"
secondary:
- "What are the key themes in patient narratives about care quality?"
- "How do quantitative satisfaction scores relate to qualitative experiences?"
methodology:
design: "Sequential Explanatory Mixed Methods"
quantitative_component:
sample_size: 500
power_analysis: "80% power to detect effect size d=0.3"
primary_outcome: "Patient satisfaction scores (HCAHPS)"
statistical_plan: "Multilevel regression with random effects for units"
qualitative_component:
sample_size: 48
sampling_strategy: "Maximum variation sampling"
data_collection: "Semi-structured interviews"
analysis_plan: "Thematic analysis using Braun & Clarke framework"
analysis_plan:
integration_approach: "Joint displays and narrative weaving"
software: ["R 4.3.0", "Python 3.9", "NVivo 12"]
reproducibility: "All code available on GitHub with Docker containers"
ethical_considerations:
irb_approval: "University IRB #2024-123"
consent_process: "Written informed consent with opt-out provisions"
data_protection: "AES-256 encryption, access controls"
community_benefit: "Results shared with patient advisory council"Theoretical Framework:
Quantum superposition principles applied to qualitative coding:
\[|\text{Code}\rangle = \alpha|\text{Theme}_1\rangle + \beta|\text{Theme}_2\rangle + \gamma|\text{Theme}_3\rangle,\] where \(|\alpha|^2 + |\beta|^2 + |\gamma|^2 = 1\). This allows for simultaneous membership in multiple themes with probability amplitudes.
Implementation:
import numpy as np
from qiskit import QuantumCircuit, ClassicalRegister, QuantumRegister
class QuantumQualitativeAnalyzer:
def __init__(self, num_themes):
self.num_themes = num_themes
self.num_qubits = int(np.ceil(np.log2(num_themes)))
def encode_narrative_superposition(self, narrative_features):
"""
Encode narrative in quantum superposition of themes
"""
qr = QuantumRegister(self.num_qubits)
cr = ClassicalRegister(self.num_qubits)
qc = QuantumCircuit(qr, cr)
# Initialize superposition
qc.h(qr)
# Apply narrative-specific rotations
for i, feature in enumerate(narrative_features):
qc.ry(feature * np.pi, qr[i % self.num_qubits])
# Entangle themes
for i in range(self.num_qubits - 1):
qc.cx(qr[i], qr[i + 1])
return qc
def measure_theme_probabilities(self, quantum_circuit):
"""
Collapse superposition to extract theme probabilities
"""
# Simulate quantum measurement
backend = Aer.get_backend('statevector_simulator')
job = execute(quantum_circuit, backend)
result = job.result()
statevector = result.get_statevector()
# Convert to theme probabilities
probabilities = np.abs(statevector) ** 2
return probabilities[:self.num_themes]Technical Specifications:
class VRInterviewEnvironment:
def __init__(self):
self.headset = VRHeadset(resolution="2160x1200", refresh_rate=90)
self.haptic_feedback = HapticController()
self.eye_tracker = EyeTracker(frequency=120)
self.physiological_monitor = BiometricSensor()
def create_contextual_environment(self, interview_context):
"""
Generate VR environment matching interview context
"""
if interview_context == "healthcare":
environment = Healthcare3DEnvironment(
lighting="soft_medical",
sounds="ambient_hospital",
objects=["virtual_medical_equipment", "comfort_items"]
)
elif interview_context == "home":
environment = Home3DEnvironment(
lighting="warm_residential",
sounds="home_ambient",
objects=["family_photos", "comfortable_furniture"]
)
return environment
def conduct_interview(self, participant, interviewer, environment):
"""
Conduct VR-mediated interview with multimodal data collection
"""
session_data = {
'audio': [],
'gaze_patterns': [],
'physiological_responses': [],
'spatial_behavior': [],
'interaction_logs': []
}
# Start recording all modalities
self.start_multimodal_recording()
# Interview protocol with adaptive branching
for question in self.adaptive_question_sequence:
# Present question in VR space
self.display_question(question, environment)
# Collect response and behavioral data
response = self.collect_response(participant)
gaze_data = self.eye_tracker.get_current_gaze()
physio_data = self.physiological_monitor.get_current_state()
session_data['audio'].append(response)
session_data['gaze_patterns'].append(gaze_data)
session_data['physiological_responses'].append(physio_data)
# Adaptive follow-up based on response sentiment
sentiment_score = self.real_time_sentiment_analysis(response)
if sentiment_score < 0.3:
follow_up = self.generate_empathetic_follow_up(response)
self.ask_follow_up(follow_up)
return session_dataImplementation:
from web3 import Web3
import hashlib
import json
class ResearchTransparencyBlockchain:
def __init__(self, web3_provider):
self.w3 = Web3(Web3.HTTPProvider(web3_provider))
self.contract_address = "0x..." # Smart contract address
def register_study_protocol(self, protocol_data):
"""
Immutably register study protocol on blockchain
"""
# Hash protocol for integrity verification
protocol_hash = hashlib.sha256(
json.dumps(protocol_data, sort_keys=True).encode()
).hexdigest()
# Create blockchain transaction
transaction = {
'study_id': protocol_data['study_id'],
'protocol_hash': protocol_hash,
'timestamp': int(time.time()),
'investigators': protocol_data['investigators'],
'research_questions': protocol_data['research_questions'],
'methodology': protocol_data['methodology']
}
# Submit to blockchain
tx_hash = self.submit_transaction(transaction)
return tx_hash, protocol_hash
def register_analysis_plan(self, study_id, analysis_plan):
"""
Register analysis plan before data collection
"""
plan_hash = hashlib.sha256(
json.dumps(analysis_plan, sort_keys=True).encode()
).hexdigest()
transaction = {
'study_id': study_id,
'plan_hash': plan_hash,
'timestamp': int(time.time()),
'analysis_type': analysis_plan['type'],
'statistical_methods': analysis_plan['methods']
}
return self.submit_transaction(transaction)
def verify_research_integrity(self, study_id, submitted_results):
"""
Verify research integrity against pre-registered plans
"""
# Retrieve blockchain records
protocol_record = self.get_study_record(study_id, 'protocol')
analysis_record = self.get_study_record(study_id, 'analysis_plan')
# Check for deviations
deviations = self.check_deviations(
protocol_record, analysis_record, submitted_results
)
return {
'integrity_score': self.calculate_integrity_score(deviations),
'deviations': deviations,
'verification_timestamp': int(time.time())
}Architecture:
class FederatedQualitativeAnalysis:
def __init__(self, num_sites):
self.num_sites = num_sites
self.global_model = None
self.site_models = [None] * num_sites
def federated_theme_discovery(self, local_datasets):
"""
Discover themes across sites without sharing raw data
"""
# Initialize global vocabulary
global_vocab = self.initialize_global_vocabulary()
for round_num in range(self.num_rounds):
# Local training at each site
local_updates = []
for site_id, dataset in enumerate(local_datasets):
# Train local model
local_model = self.train_local_theme_model(
dataset, global_vocab, self.global_model
)
# Extract model parameters (not raw data)
local_update = self.extract_model_parameters(local_model)
local_updates.append(local_update)
# Aggregate updates at central server
self.global_model = self.federated_averaging(local_updates)
# Distribute updated global model
self.broadcast_global_model()
return self.extract_global_themes()
def privacy_preserving_aggregation(self, local_parameters):
"""
Aggregate model parameters with differential privacy
"""
# Add noise for differential privacy
noise_scale = self.calculate_noise_scale(
epsilon=1.0, # Privacy budget
delta=1e-5, # Privacy parameter
sensitivity=self.calculate_sensitivity()
)
# Aggregate with noise
aggregated_params = {}
for param_name in local_parameters[0].keys():
# Average parameters across sites
avg_param = np.mean([
params[param_name] for params in local_parameters
], axis=0)
# Add calibrated noise
noise = np.random.laplace(0, noise_scale, avg_param.shape)
aggregated_params[param_name] = avg_param + noise
return aggregated_paramsThe convergence of artificial intelligence, participatory research methods, and traditional qualitative approaches represents a paradigm shift in healthcare research. Key recommendations include:
Technical Integration:
Methodological Rigor:
Ethical Frameworks:
Phase 1 (Months 1-6): Foundation Building - Establish technical infrastructure - Train research teams in advanced methods - Develop ethical protocols and IRB procedures - Create community partnerships
Phase 2 (Months 7-18): Pilot Implementation - Conduct small-scale studies using integrated methods - Validate AI-enhanced analysis pipelines - Refine participatory co-design processes - Establish quality assurance protocols
Phase 3 (Months 19-36): Scale-up and Evaluation - Implement large-scale multi-site studies - Evaluate method effectiveness and efficiency - Develop standardized protocols and training curricula - Disseminate findings and best practices
The implementation of these advanced qualitative and mixed methods approaches is expected to:
The future of qualitative research in healthcare lies in the thoughtful integration of technological innovation with human-centered approaches, maintaining the rich contextual understanding that qualitative methods provide while leveraging computational power to enhance analysis depth and breadth.
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77-101.
Creswell, J. W., & Plano Clark, V. L. (2017). Designing and conducting mixed methods research (3rd ed.). Sage Publications.
Tashakkori, A., & Teddlie, C. (Eds.). (2010). Sage handbook of mixed methods in social & behavioral research (2nd ed.). Sage.
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage Publications.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993-1022.
Vaswani, A., Shazeer, N., Parmar, N., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 5998-6008.
Iedema, R., Mesman, J., & Carroll, K. (2013). Visualising health care practice improvement. Radcliffe Publishing.
Carroll, K., Iedema, R., & Kerridge, R. (2008). Reshaping ICU ward round practices using video-reflexive ethnography. Qualitative Health Research, 18(3), 380-390.
Hektner, J. M., Schmidt, J. A., & Csikszentmihalyi, M. (2007). Experience sampling method: Measuring the quality of everyday life. Sage Publications.
Pink, S., Horst, H., Postill, J., et al. (2015). Digital ethnography: Principles and practice. Sage Publications.
Sanders, E. B. N., & Stappers, P. J. (2008). Co-creation and the new landscapes of design. CoDesign, 4(1), 5-18.
Israel, B. A., Eng, E., Schulz, A. J., & Parker, E. A. (Eds.). (2012). Methods for community-based participatory research for health (2nd ed.). Jossey-Bass.
Raudenbush, S. W., & Bryk, A. S. (2002). Hierarchical linear models: Applications and data analysis methods (2nd ed.). Sage Publications.
Kline, R. B. (2015). Principles and practice of structural equation modeling (4th ed.). Guilford Publications.
Newman, M. E. (2018). Networks (2nd ed.). Oxford University Press.
Python Software Foundation. (2023). Python 3.9 documentation. https://docs.python.org/3.9/
Hugging Face. (2023). Transformers documentation. https://huggingface.co/docs/transformers
R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing.
McKinney, W. (2010). Data structures for statistical computing in Python. Proceedings of the 9th Python in Science Conference, 56-61.
Complete Installation Script:
#!/bin/bash
# install_environment.sh
# Create conda environment
conda create -n qualitative-research python=3.9 -y
conda activate qualitative-research
# Core data science packages
conda install -c conda-forge pandas numpy scipy scikit-learn matplotlib seaborn plotly -y
# Natural language processing
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install transformers datasets tokenizers
pip install nltk spacy
python -m spacy download en_core_web_sm
# Qualitative analysis specific
pip install textblob vaderSentiment
pip install networkx community
pip install gensim
# Video and multimedia
pip install opencv-python moviepy
pip install librosa soundfile
# Jupyter and development
conda install -c conda-forge jupyter jupyterlab -y
pip install ipywidgets
# Database and storage
pip install sqlalchemy psycopg2-binary
# Web frameworks (for data collection interfaces)
pip install streamlit flask fastapi
# Statistical packages
pip install statsmodels pingouin
echo "Environment setup complete!"
echo "Activate with: conda activate qualitative-research"Required R Packages:
# install_r_packages.R
# Core packages
required_packages <- c(
"tidyverse", "dplyr", "ggplot2", "readr",
"lavaan", "semTools", "psych", "GPArotation",
"lme4", "nlme", "brms", "rstanarm",
"igraph", "network", "visNetwork",
"tm", "tidytext", "topicmodels", "stm",
"qualitative", "RQDA", "qgraph",
"knitr", "rmarkdown", "bookdown",
"DT", "plotly", "shiny", "shinydashboard"
)
# Function to install packages if not already installed
install_if_missing <- function(packages) {
new_packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new_packages)) {
install.packages(new_packages, dependencies = TRUE)
}
}
# Install packages
install_if_missing(required_packages)
# Load and test key packages
library(tidyverse)
library(lavaan)
library(lme4)
cat("R environment setup complete!\n")PostgreSQL Setup for Qualitative Data:
-- create_qualitative_database.sql
-- Create database
CREATE DATABASE qualitative_research;
-- Connect to database
\c qualitative_research;
-- Create schemas
CREATE SCHEMA raw_data;
CREATE SCHEMA processed_data;
CREATE SCHEMA analysis_results;
-- Participants table
CREATE TABLE raw_data.participants (
participant_id VARCHAR(50) PRIMARY KEY,
study_id VARCHAR(50) NOT NULL,
demographics JSONB,
consent_date TIMESTAMP,
enrollment_date TIMESTAMP,
status VARCHAR(20) DEFAULT 'active',
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Qualitative data table
CREATE TABLE raw_data.qualitative_data (
data_id VARCHAR(50) PRIMARY KEY,
participant_id VARCHAR(50) REFERENCES raw_data.participants(participant_id),
data_type VARCHAR(50) NOT NULL, -- 'interview', 'observation', 'diary', 'photo'
content TEXT,
metadata JSONB,
file_path VARCHAR(500),
collection_timestamp TIMESTAMP,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Codes table
CREATE TABLE processed_data.codes (
code_id VARCHAR(50) PRIMARY KEY,
data_id VARCHAR(50) REFERENCES raw_data.qualitative_data(data_id),
code_text VARCHAR(200) NOT NULL,
code_category VARCHAR(100),
start_position INTEGER,
end_position INTEGER,
coder_id VARCHAR(50),
coding_method VARCHAR(50), -- 'manual', 'ai_assisted', 'automated'
confidence_score DECIMAL(3,2),
coding_timestamp TIMESTAMP,
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Themes table
CREATE TABLE processed_data.themes (
theme_id VARCHAR(50) PRIMARY KEY,
theme_name VARCHAR(200) NOT NULL,
theme_description TEXT,
parent_theme_id VARCHAR(50) REFERENCES processed_data.themes(theme_id),
level INTEGER DEFAULT 1,
created_by VARCHAR(50),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Code-theme relationships
CREATE TABLE processed_data.code_theme_relations (
relation_id VARCHAR(50) PRIMARY KEY,
code_id VARCHAR(50) REFERENCES processed_data.codes(code_id),
theme_id VARCHAR(50) REFERENCES processed_data.themes(theme_id),
relationship_type VARCHAR(50), -- 'belongs_to', 'supports', 'contradicts'
strength DECIMAL(3,2),
created_at TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Analysis results
CREATE TABLE analysis_results.sentiment_analysis (
analysis_id VARCHAR(50) PRIMARY KEY,
data_id VARCHAR(50) REFERENCES raw_data.qualitative_data(data_id),
sentiment_score DECIMAL(5,4),
sentiment_label VARCHAR(20),
confidence DECIMAL(3,2),
model_version VARCHAR(50),
analysis_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Network analysis results
CREATE TABLE analysis_results.network_metrics (
metric_id VARCHAR(50) PRIMARY KEY,
participant_id VARCHAR(50) REFERENCES raw_data.participants(participant_id),
metric_type VARCHAR(50), -- 'centrality', 'clustering', 'connectivity'
metric_value DECIMAL(10,6),
network_type VARCHAR(50), -- 'semantic', 'social', 'temporal'
calculation_timestamp TIMESTAMP DEFAULT CURRENT_TIMESTAMP
);
-- Create indexes for performance
CREATE INDEX idx_qualitative_data_participant ON raw_data.qualitative_data(participant_id);
CREATE INDEX idx_qualitative_data_type ON raw_data.qualitative_data(data_type);
CREATE INDEX idx_codes_data_id ON processed_data.codes(data_id);
CREATE INDEX idx_codes_category ON processed_data.codes(code_category);
CREATE INDEX idx_sentiment_data_id ON analysis_results.sentiment_analysis(data_id);
-- Create views for common queries
CREATE VIEW processed_data.participant_summary AS
SELECT
p.participant_id,
p.study_id,
COUNT(DISTINCT qd.data_id) as total_data_points,
COUNT(DISTINCT c.code_id) as total_codes,
AVG(sa.sentiment_score) as avg_sentiment
FROM raw_data.participants p
LEFT JOIN raw_data.qualitative_data qd ON p.participant_id = qd.participant_id
LEFT JOIN processed_data.codes c ON qd.data_id = c.data_id
LEFT JOIN analysis_results.sentiment_analysis sa ON qd.data_id = sa.data_id
GROUP BY p.participant_id, p.study_id;
GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA raw_data TO qualitative_user;
GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA processed_data TO qualitative_user;
GRANT ALL PRIVILEGES ON ALL TABLES IN SCHEMA analysis_results TO qualitative_user;JSON Structure for Interview Data:
{
"interview_metadata": {
"interview_id": "INT_2024_001",
"participant_id": "P_047",
"interviewer_id": "INT_JS",
"date": "2024-03-15",
"duration_minutes": 67,
"location": "participant_home",
"recording_quality": "high",
"transcription_method": "automated_with_human_review",
"language": "en-US"
},
"participant_demographics": {
"age_range": "65-74",
"gender": "female",
"ethnicity": "hispanic",
"education": "high_school",
"income_range": "30k-50k",
"health_conditions": ["diabetes", "hypertension"],
"technology_comfort": 3
},
"transcript_segments": [
{
"segment_id": "SEG_001",
"timestamp_start": "00:02:15",
"timestamp_end": "00:02:47",
"speaker": "interviewer",
"content": "Can you tell me about your experience managing your diabetes over the past year?",
"speech_features": {
"tone": "neutral",
"pace": "normal",
"volume": "medium"
}
},
{
"segment_id": "SEG_002",
"timestamp_start": "00:02:48",
"timestamp_end": "00:04:23",
"speaker": "participant",
"content": "Well, it's been really challenging, especially with the medication costs. Sometimes I have to choose between buying my insulin and paying for other necessities. The doctor doesn't seem to understand how hard it is.",
"speech_features": {
"tone": "frustrated",
"pace": "varied",
"volume": "rising",
"emotional_markers": ["sigh", "pause_3sec"]
},
"codes": [
{
"code": "financial_barriers",
"start_char": 45,
"end_char": 142,
"coder": "human_coder_1",
"confidence": 0.95
},
{
"code": "provider_communication",
"start_char": 190,
"end_char": 245,
"coder": "human_coder_1",
"confidence": 0.88
}
]
}
],
"interview_summary": {
"main_themes": ["financial_barriers", "provider_communication", "family_support"],
"sentiment_overall": "negative",
"key_insights": [
"Cost of medication is primary barrier",
"Communication gaps with healthcare provider",
"Strong family support system present"
],
"follow_up_needed": true,
"member_check_completed": false
}
}ESM Response Structure:
{
"response_metadata": {
"response_id": "ESM_2024_P047_0156",
"participant_id": "P_047",
"prompt_id": "PROMPT_MED_ADHERENCE_01",
"timestamp": "2024-03-15T14:30:00Z",
"response_time_seconds": 45,
"app_version": "1.2.3",
"device_info": {
"os": "iOS 17.1",
"model": "iPhone 12",
"screen_size": "6.1_inch"
}
},
"prompt_details": {
"prompt_type": "medication_reminder",
"prompt_text": "Did you take your morning medication as prescribed?",
"trigger_condition": "time_based",
"scheduled_time": "14:30:00",
"actual_delivery_time": "14:30:02"
},
"responses": {
"took_medication": {
"response": "yes",
"response_type": "binary",
"confidence": "certain"
},
"difficulty_level": {
"response": 2,
"response_type": "likert_7",
"scale_labels": ["very_easy", "very_difficult"]
},
"side_effects": {
"response": "mild_nausea",
"response_type": "multiple_choice",
"options": ["none", "mild_nausea", "dizziness", "fatigue", "other"]
},
"mood": {
"response": 6,
"response_type": "likert_10",
"scale_labels": ["very_sad", "very_happy"]
},
"context_location": {
"response": "home",
"response_type": "categorical",
"gps_enabled": false
},
"free_text": {
"response": "Remembered because of calendar alert, took with breakfast",
"response_type": "open_text",
"character_count": 58
}
},
"sensor_data": {
"location": {
"latitude": 42.3601,
"longitude": -71.0589,
"accuracy_meters": 5.0,
"altitude_meters": 15.2,
"timestamp": "2024-03-15T14:30:00Z"
},
"activity": {
"activity_type": "stationary",
"confidence": 0.92,
"steps_last_hour": 247
},
"ambient": {
"light_level": "bright",
"noise_level_db": 45,
"estimated_indoor": true
}
},
"derived_variables": {
"adherence_streak": 7,
"mood_trend_7d": "stable",
"response_pattern_deviation": 0.12,
"context_consistency": 0.85
}
}Complete Mathematical Derivation:
For a two-level model with qualitative outcomes coded as numeric values:
Level 1 (Within-person/unit):
\(Y_{ij} = \pi_{0j} + \pi_{1j}(X_{1ij}) + \pi_{2j}(X_{2ij}) + \ldots + \pi_{pj}(X_{pij}) + e_{ij}\)
Level 2 (Between-person/unit):
\(\pi_{0j} = \beta_{00} + \beta_{01}(W_{1j}) + \beta_{02}(W_{2j}) + \ldots + \beta_{0q}(W_{qj}) + r_{0j}\) \(\pi_{1j} = \beta_{10} + \beta_{11}(W_{1j}) + \beta_{12}(W_{2j}) + \ldots + \beta_{1q}(W_{qj}) + r_{1j}\) \(\vdots\) \(\pi_{pj} = \beta_{p0} + \beta_{p1}(W_{1j}) + \beta_{p2}(W_{2j}) + \ldots + \beta_{pq}(W_{qj}) + r_{pj}\)
Combined Model:
\(\begin{align} Y_{ij} &= \beta_{00} + \beta_{01}(W_{1j}) + \ldots + \beta_{0q}(W_{qj}) \\ &\quad + \beta_{10}(X_{1ij}) + \beta_{11}(W_{1j})(X_{1ij}) + \ldots + \beta_{1q}(W_{qj})(X_{1ij}) \\ &\quad + \ldots \\ &\quad + \beta_{p0}(X_{pij}) + \beta_{p1}(W_{1j})(X_{pij}) + \ldots + \beta_{pq}(W_{qj})(X_{pij}) \\ &\quad + r_{0j} + r_{1j}(X_{1ij}) + \ldots + r_{pj}(X_{pij}) + e_{ij} \end{align}\)
Variance Components:
\(\text{Var}(Y_{ij}) = \text{Var}(r_{0j} + r_{1j}(X_{1ij}) + \ldots + r_{pj}(X_{pij}) + e_{ij}) = \tau_{00} + 2\tau_{01}(X_{1ij}) + \tau_{11}(X_{1ij})^2 + \ldots + \sigma^2\)
Intraclass Correlation:
\(\text{ICC} = \frac{\tau_{00}}{\tau_{00} + \sigma^2}\)
Likelihood Function for Maximum Likelihood Estimation:
\[L = \prod_{j=1}^J \int \prod_{i=1}^{n_j} f(Y_{ij} | \pi_j, \sigma^2) \times g(\pi_j | \beta, T) d\pi_j,\] where: - \(f(Y_{ij} | \pi_j, \sigma^2)\) is the level-1 conditional distribution - \(g(\pi_j | \beta, T)\) is the level-2 distribution of random effects
Entropy for Qualitative Coding:
\(H(X) = -\sum_{i=1}^n p(x_i) \log_2 p(x_i)\)
where \(p(x_i)\) = probability of code/theme \(i\)
Conditional Entropy:
\(H(Y|X) = -\sum\sum p(x,y) \log_2 p(y|x)\)
Mutual Information:
\(I(X;Y) = H(X) - H(X|Y) = H(Y) - H(Y|X) = \sum\sum p(x,y) \log_2 \left[\frac{p(x,y)}{p(x)p(y)}\right]\)
Normalized Mutual Information:
\(\text{NMI}(X,Y) = \frac{I(X;Y)}{\sqrt{H(X)H(Y)}}\)
Centrality Measures:
Degree Centrality: \(C_D(v) = \frac{\deg(v)}{n-1}\) where \(\deg(v)\) = number of edges incident to vertex \(v\)
Betweenness Centrality: \(C_B(v) = \sum_{s \neq v \neq t} \frac{\sigma_{st}(v)}{\sigma_{st}}\), where:
Closeness Centrality:
\[C_C(v) = \frac{n-1}{\sum_{u \neq v} d(v,u)},\]
where \(d(v,u)\) = shortest path distance between \(v\) and \(u\)
Eigenvector Centrality:
\[C_E(v) = \frac{1}{\lambda} \sum_{u \in N(v)} C_E(u),\] where \(\lambda\) is the largest eigenvalue and \(N(v)\) are neighbors of \(v\)
Community Detection (Modularity):
\[Q = \frac{1}{2m} \sum_{ij} \left[A_{ij} - \frac{k_i k_j}{2m}\right] \delta(c_i, c_j),\] where:
Protocol for Establishing Coding Reliability:
Phase 1: Training and Calibration (Week 1)
Phase 2: Initial Reliability Testing (Week 2)
\(\kappa = \frac{P_o - P_e}{1 - P_e}\)
\(\alpha = 1 - \frac{D_o}{D_e}\)
\(\text{PA} = \frac{\text{Number of agreements}}{\text{Total comparisons}}\)
Phase 3: Ongoing Monitoring (Throughout Study)
Statistical Implementation:
def calculate_reliability_metrics(coder1_labels, coder2_labels):
"""
Calculate comprehensive inter-rater reliability metrics
"""
from sklearn.metrics import cohen_kappa_score
import krippendorff
# Cohen's Kappa
kappa = cohen_kappa_score(coder1_labels, coder2_labels)
# Krippendorff's Alpha
reliability_data = [coder1_labels, coder2_labels]
alpha = krippendorff.alpha(reliability_data, level_of_measurement='nominal')
# Percentage Agreement
agreements = sum(c1 == c2 for c1, c2 in zip(coder1_labels, coder2_labels))
total_comparisons = len(coder1_labels)
percent_agreement = agreements / total_comparisons
# Interpretation
kappa_interpretation = interpret_kappa(kappa)
return {
'cohens_kappa': kappa,
'krippendorffs_alpha': alpha,
'percent_agreement': percent_agreement,
'interpretation': kappa_interpretation,
'meets_threshold': kappa >= 0.80 and alpha >= 0.80 and percent_agreement >= 0.90
}
def interpret_kappa(kappa_value):
"""Interpret Cohen's Kappa values"""
if kappa_value < 0:
return "Poor agreement (worse than chance)"
elif kappa_value < 0.20:
return "Slight agreement"
elif kappa_value < 0.40:
return "Fair agreement"
elif kappa_value < 0.60:
return "Moderate agreement"
elif kappa_value < 0.80:
return "Substantial agreement"
else:
return "Almost perfect agreement"Level 1: Foundational Knowledge (60 hours)
Module 1: Qualitative Research Foundations (20 hours) - Philosophical underpinnings of qualitative research - Epistemological and ontological considerations - Quality criteria: credibility, transferability, dependability, confirmability - Ethical considerations in qualitative research - Introduction to mixed methods research
Module 2: Data Collection Methods (20 hours) - Interview techniques and best practices - Observation methods and field notes - Focus group facilitation - Digital data collection considerations - Participant recruitment and retention
Module 3: Basic Analysis Techniques (20 hours) - Thematic analysis fundamentals - Coding strategies and techniques - Data management and organization - Introduction to qualitative software (NVivo, Atlas.ti) - Quality assurance in coding
Level 2: Intermediate Skills (120 hours)
Module 4: Advanced Analysis Methods (40 hours) - Grounded theory methodology - Phenomenological analysis - Discourse analysis - Content analysis - Framework analysis
Module 5: Technology Integration (30 hours) - Digital data collection platforms - Automated transcription tools - Basic natural language processing - Video analysis software - Cloud-based collaboration tools
Module 6: Mixed Methods Design (30 hours) - Sequential designs (explanatory, exploratory) - Concurrent designs (triangulation, embedded) - Transformative frameworks - Integration strategies - Quality assessment in mixed methods
Module 7: Statistical Foundations (20 hours) - Descriptive statistics for qualitative data - Basic inferential statistics - Multilevel modeling concepts - Network analysis fundamentals - Visualization techniques
Level 3: Advanced Practice (200 hours)
Module 8: AI-Enhanced Analysis (60 hours) - Machine learning for text analysis - Sentiment analysis implementation - Topic modeling with LDA - Neural network architectures for NLP - Bias detection and mitigation
Module 9: Specialized Methods (60 hours) - Video-reflexive ethnography - Mobile ethnography and ESM - Participatory action research - Digital ethnography - Virtual reality applications
Module 10: Advanced Statistical Methods (50 hours) - Structural equation modeling - Multilevel analysis with qualitative outcomes - Bayesian approaches to qualitative analysis - Network analysis and visualization - Time series analysis for longitudinal qualitative data
Module 11: Research Leadership (30 hours) - Grant writing for qualitative research - Team management and collaboration - Community engagement strategies - Dissemination and knowledge translation - Mentoring and supervision
Level 4: Expert Certification (300 hours)
Module 12: Methodological Innovation (80 hours) - Developing new analytical approaches - Validation of novel methods - Cross-cultural adaptation of methods - Technology development for research - Open science and reproducibility
Module 13: Advanced Ethics and Governance (60 hours) - Complex ethical scenarios - International research ethics - Indigenous research methodologies - Data sovereignty and governance - Algorithmic ethics and AI governance
Module 14: Research Translation (80 hours) - Policy impact assessment - Stakeholder engagement strategies - Implementation science methods - Scaling and sustainability planning - Evaluation and outcome measurement
Module 15: Capstone Project (80 hours) - Independent research project - Methodology development or validation - Peer review and presentation - Portfolio development - Certification examination
The Nurse AI Trainer (NAIT) offers interactive examples, data, and hands-on training in qualitative and mixed data analytics, from the NAIT Modules, select NAIT Module 7.