<p>Accountability in patient adherence</p>
Bibliographic record
Abstract
BACKGROUND: The accountability inherent in the social interaction between a patient and healthcare provider affects patients' motivation to adhere to treatment. To characterize the role of accountability as a tool to improve self-efficacy and self-management and thereby promote patients' adherence to treatment, a measure of accountability is needed. AIMS: To develop and test the validity, reliability, and sensitivity of a new outcome measure designed to assess accountability. METHODS: The accountability measurement tool was developed from the literature, expert consultation, and focus groups. A focus group and three pilot studies were performed both in clinic and through an online crowdsourcing platform. Principal Component Analysis evaluated constructs, and Cronbach's alpha measured internal consistency. Validity was established using convergent and divergent correlations to other validated scales. RESULTS: A total of 292 participants took part in this study. The 12-item accountability scale demonstrated very good internal consistency (Cronbach's α=0.92). Components of the accountability measurement tool correlated with predicted validated measures, including the Treatment Self-Regulation Questionnaire. Divergent validity was established with no significant difference noted between age, sex, race, and education level. CONCLUSION: Future use of this questionnaire will allow for the assessment of the interaction between accountability and adherence to treatment and lead to the development of new interventions to promote better adherence.
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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 | 0.014 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".