Health Beliefs, Disease Severity, and Patient Adherence
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it