Development and Validation of a Novel Tool to Measure Medication Adherence for Noncommunicable Diseases in India: Protocol for an Exploratory Sequential Mixed Methods Multicentric Study
Bibliographic record
Abstract
BACKGROUND: In high-income countries, only 50% of patients treated for chronic diseases adhere to the prescribed treatment. This issue is even more pronounced in resource-limited countries. Medication adherence scales are simple, low-cost approaches to identify nonadherence in clinical practice. In India, nonadherence to medication varies from 18.7% to 74%, assessed using scales validated in the Western population, as there is no validated medication adherence tool contextualized to the Indian setting. The phrasing of questions in scales validated elsewhere and its interpretations may vary when applied in Indian patients unless accounting for the unique cultural, social, and economic factors influencing medication adherence in India. This could result in inaccurate reports of adherence behavior. OBJECTIVE: This study aims to develop and validate a novel medication adherence tool for select noncommunicable diseases (diabetes mellitus, hypertension, chronic obstructive pulmonary disease, bronchial asthma, and coronary artery disease) in the Indian population. METHODS: An exploratory sequential mixed methods design will be used, beginning with a qualitative phase where the construct of the scale is defined and preliminary items are generated through a scoping review, focus group discussions, and in-depth interviews. This will be followed by the tool's development phase, including an expert panel review and item revision. Finally, a quantitative phase in 4 zones in India (North, South, East, and West) will be conducted to confirm and validate the newly developed scale. RESULTS: In the first phase, we will frame the construct definition and develop an inventory of potential items for the proposed medication adherence tool. In the second phase, item-level and scale-level content validity indices, along with content validity ratio, will be estimated. In the third phase, we will conduct an item reduction analysis and determine the scoring matrix and item weightage after expert review. We will assess the tool's psychometric properties, plot the receiver operating characteristic (ROC) curve to set an adherence cut-off score, and compute the construct validity and test-retest reliability from the quantitative survey. CONCLUSIONS: A medication adherence tool for noncommunicable diseases, developed after ensuring it is ethnically, culturally, and linguistically appropriate incorporating stakeholder perspectives and validated in community settings, would offer a real-world perspective of adherence. The tool will have 2 versions for clinical practice and research, aiding policy makers in adopting tailored adherence policies. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): PRR1-10.2196/60805.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.060 | 0.045 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".