Barriers and facilitators to implementation of a metabolic monitoring protocol in hospital and community settings for second-generation antipsychotic-treated youth.
Bibliographic record
Abstract
OBJECTIVE: 1) Assess perceived barriers associated with metabolic monitoring in second-generation antipsychotic (SGA)-treated youth; and 2) Propose a metabolic monitoring protocol (MMP) and implementation strategies. METHOD: Online surveys were created for community mental health teams (CMHTs) and BC Children's Hospital (BCCH) with questions designed to evaluate knowledge of physical health care, confidence, communication with primary care, and practical issues. RESULTS: 26/50 (52%) of CMHT and 44/111 (40%) of BCCH surveys were completed. While both groups agreed that monitoring is their responsibility, 26% of CMHTs and 35% of BCCH professionals agreed that providing information about SGA side-effects would influence medication adherence. CMHTs reported lower overall confidence and more practical issues as monitoring barriers. While higher overall confidence was reported at BCCH, there was still a substantial proportion (23%) of hospital professionals who reported not knowing what parameters to monitor and how frequently. Communication with primary care, including inadequate systems for sharing results and identifying responsibility for acting on abnormal results, appear to be common barriers shared by both settings. CONCLUSIONS: Barriers to metabolic monitoring were more frequently reported by CMHTs who had limited access to nursing staff. We propose hands-on training, educational resources, pre-printed orders, and regular quality assurance evaluation as facilitators to promote MMP uptake.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".