Psychotropic medication monitoring checklists: use and utility for children in residential care.
Bibliographic record
Abstract
OBJECTIVE: To develop side effect (SE) monitoring checklists for four categories of psychotropic medications (antipsychotics, mood stabilizers, stimulants, and selective serotonin-reuptake inhibitors), to improve residential direct care staff's confidence and competence in SE monitoring, and to facilitate communication of potential observed SE to medical personnel (e.g., nurse, physician). METHODS: Seventy-two staff members (three nurses, 69 child/youth workers) from five residential units at a tertiary mental health centre utilized the Psychotropic Medication Monitoring Checklists (PMMC) for eight weeks and completed pre- and post-test measures of staff characteristics and PMMC satisfaction. RESULTS: The use of PMMC led to significant changes in direct care staff's awareness and beliefs associated with medication monitoring. An increase in staff-physician communication with direct care staff was marginally significant. Further investigation into the educational qualities of the PMMC revealed that staff with very little prior formal medication education showed greater change compared to those staff reporting greater formal medication instruction. Staff ratings of the PMMC exceeded mild levels of satisfaction, indicating that the checklists were a well-received and useful tool for monitoring SE in a residential care setting. CONCLUSIONS: The PMMC are useful as an educational SE monitoring tool for direct care staff in child residential care settings, with potential utility for multiple types of healthcare settings.
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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 | 0.005 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".