An approach to maximizing treatment adherence of children and adolescents with psychotic disorders and major mood disorders.
Bibliographic record
Abstract
INTRODUCTION: Mental health research has consistently focused on high rates of treatment non-adherence, and how inpatient programs and health professionals can effectively confront this reality. The literature has centred almost exclusively on adult populations. Unfortunately, psychotic and major mood disorders are serious and persistent mental health problems that are increasingly recognized as having an early onset, affecting children and adolescents. METHOD: This article draws on a review of the literature and Habermas's three domains of knowledge: technical, practical, and emancipatory. This article has incorporated current research, adherence theories, and the facilitation of the unique local expertise of health professionals to generate a framework. This framework is designed to teach health professionals working in child and adolescent psychiatric inpatient units how they and the larger unit can practice to enhance patient treatment adherence during and after admission. RESULTS: A five-step approach to teach health professionals to enhance treatment adherence has been developed based on current research and educational theories and principles. CONCLUSION: Health professionals working in child and adolescent psychiatry can practice to enhance patient treatment adherence, and improve patient and family outcomes.
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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 | 0.003 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".