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
Health care professionals must be alert to the high prevalence of low adherence to treatment during adolescence. Low adherence increases morbidity and medical complications, contributes to poorer quality of life and an overuse of the health care system. Many different factors have an impact on adherence. However, critical factors to consider in teens are their developmental stage and challenges, emotional issues and family dysfunction. Direct and indirect methods have been described to assess adherence. Eliciting an adherence history is the most useful way for clinicians to evaluate adherence, and could be the beginning of a constructive dialogue with the adolescent. Interventions to improve adherence are multiple - managing mental health issues appropriately, building a strong relationship, customizing the treatment regimen if possible, empowering the adolescent to deal with adherence issues, providing information, ensuring family and peer support, and motivational enhancement therapy. Evaluation of adherence at regular intervals should be an important aspect of health care for adolescents.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.000 | 0.002 |
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