Review of Adherence Measures for use in Phase Iv Studies and Recommendations for a new Standardized Generic Measure
Bibliographic record
Abstract
This study reviews adherence metrics for Phase IV studies. We conducted a review of adherence metrics in the public domain. We critically appraised these metrics for use in Phase IV studies. We identified 70 unique self-report measures of adherence. One quarter (26%) were generic and the remaining were disease specific. Instrument length ranged from one to 78 items. One third (34%) only measured adherence behaviors, 37% only measured beliefs and attitudes, and 29% measured both. Just over one quarter (29%) were developed using a conceptual framework. One-fifth (21%) involved qualitative patient input during item generation or pretesting. Just over one-half (57%) had evidence of internal-consistency reliability, and far fewer had evidence of test-retest reliability (23%). One half (50%) had evidence of validity vis á vis other self-report measures, 23% vis á vis other adherence metrics, and 19% vis á vis clinical criteria. Few had evidence of predictive (24%) or postdictive (13%) validity. Few adherence measures have been developed with true patient-centerednesss. There has been no standardization of the content of adherence behaviors or beliefs. Instrument validation has been inconsistent in its breadth and depth. Because of the importance of medication adherence to payers, providers, pharmacies, and pharmaceutical companies, the time seems opportune to conceptualize, develop, and validate a generic adherence measure that can be used in Phase IV studies across different disease and patient groups. Standardization of content would allow for the assessment of adherence behaviors and beliefs between and across existing and novel therapies. There should be a minimum set of adherence concepts that apply across therapeutic areas. The new measure should be developed with patient input (concept elicitation) and verified as to its comprehension and relevance using cognitive debriefing. The scientific basis of medication adherence would be advanced through the development and validation of a standardized generic measure that assesses adherence behaviors and beliefs.
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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 | 0.196 | 0.426 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.013 |
| Bibliometrics | 0.017 | 0.012 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".