Implementation of a Clozapine Clinical Toolkit at the Dubé Centre for Mental Health
Bibliographic record
Abstract
Introduction: Clozapine is the most efficacious antipsychotic and it is the only approved pharmacotherapy for treatment-resistant schizophrenia. Despite this, it is grossly underutilized as there are many barriers to its use. One barrier is health professional confidence and knowledge related to clozapine. A standardized, evidence-based protocol to care for patients on clozapine may increase appropriate use of this medication. A clozapine Clinical Toolkit (CTK) was developed and implemented in Vancouver, British Columbia, and, with permission, was adapted to the Dubé Centre for Mental Health (DCMH). Small group education session on the CTK were provided to the DCMH nursing staff. The objective of this study was to determine the impact of the education sessions on nurses’ confidence and knowledge related to clozapine. Methods: Groups of one to five nurses at the DCMH were provided mixed-media education sessions on the clozapine CTK. Sessions were led by one or two researchers and ranged between 15 to 20 minutes in duration. Pre- and post-education questionnaires were administered to assess nurses’ knowledge and confidence related to clozapine. Questionnaire completion was voluntary and anonymous. The results were analyzed using simple summary statistics. Results & Conclusion: The pre-education questionnaire was completed by 81 nurses and 80 nurses completed the post-education questionnaire. The small group, mixed-media education sessions, improved nursing knowledge on three out of five clozapine knowledge-based questions and overall enhanced nurses’ confidence related to clozapine.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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 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".