Assessing Barriers to Therapeutic Regimens for Young People with Juvenile Idiopathic Arthritis
Bibliographic record
Abstract
The Health Belief Model (HBM) has been one of the most widely used theories in health behavior research over the past 5 decades1,2. Originally developed in the early 1950s to understand why people failed to take advantage of preventive health services (such as hypertension screening), the HBM was later extended to adherence to prescribed medical regimens3,4. The HBM posits 5 major sets of variables that predict or explain adherence: (1) perceived susceptibility (including the person’s perceived risk of contracting or re-contracting a condition or acceptance of an existing condition); (2) perceived severity (the person’s evaluation of the medical and social consequences of contracting an illness or not receiving treatment); (3) perceived benefits (the person’s judgment of the perceived benefits of taking a particular health action); (4) perceived barriers (the person’s perception of impediments to adhere to recommended treatments, … Address correspondence to Dr. M.A. Rapoff, 3901 Rainbow Blvd., Kansas City, Kansas 66103-7330, USA. E-mail: mrapoff{at}kumc.edu
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".