Health Beliefs, Disease Severity, and Patient Adherence
Bibliographic record
Abstract
BACKGROUND: A large body of empirical data exists on the prediction of patient adherence from subjective and objective assessments of health status and disease severity. This work can be summarized with meta-analysis. OBJECTIVES: Retrieval and summary analysis of r effect sizes and moderators of the relationship between patient adherence and patients': (1) beliefs in disease threat; (2) rated health status (by physician, self, or parent); and (3) objective disease severity. METHODS: Comprehensive search of published literature (1948-2005) yielding 116 articles, with 143 separate effect sizes. Calculation of robust, generalizable random effects model statistics, and detailed examination of study diversity with moderator analyses. RESULTS: Adherence is significantly positively correlated with patients' beliefs in the severity of the disease to be prevented or treated ("disease threat"). Better patient adherence is associated with objectively poorer health only for patients experiencing disease conditions lower in seriousness (according to the Seriousness of Illness Rating Scale). Among conditions higher in seriousness, worse adherence is associated with objectively poorer health. Similar patterns exist when health status is rated by patients themselves, and by parents in pediatric samples. CONCLUSIONS: Results suggest that the objective severity of patients' disease conditions, and their awareness of this severity, can predict their adherence. Patients who are most severely ill with serious diseases may be at greatest risk for nonadherence to treatment. Findings can contribute to greater provider awareness of the potential for patient nonadherence, and to better targeting of health messages and treatment advice by providers.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".