Evaluation of the antipsychotic medication review process at four long-term facilities in Alberta
Bibliographic record
Abstract
PURPOSE: The goal of this evaluation was to understand how four long-term care (LTC) facilities in Alberta have implemented medication reviews for the Appropriate Use of Antipsychotics (AUA) initiative. We aimed to determine how interprofessional (IP) collaboration was incorporated in the antipsychotic medication reviews and how the reviews had been sustained. METHODS: Four LTC facilities in Alberta participated in this evaluation. We conducted semistructured interviews with 18 facility staff and observed one antipsychotic medication review at each facility. We analyzed data according to the following key components that we identified as relevant to the antipsychotic medication reviews: the structure of the reviews, IP interactions between the staff members, and strategies for sustaining the reviews. RESULTS: The duration of antipsychotic medication reviews ranged from 1 to 1.5 hours. The number of professions in attendance ranged from 3 to 9; a pharmacist led the review at two sites, while a registered nurse led the review at one site and a nurse practitioner at the remaining site. The number of residents discussed during the review ranged from 6 to 20. The process at some facilities was highly IP, demonstrating each of the six IP competencies. Other facilities conducted the review in a less IP manner due to challenges of physician involvement and staff workload, particularly of health care aides. Facilities that had an nurse practitioner on site were more efficient with the process of implementing recommendations resulting from the medication reviews. CONCLUSION: The LTC facilities were successful in implementing the medication review process and the process seemed to be sustainable. A few challenges were observed in the implementation process at two facilities. IP practice moved forward the goals of the AUA initiative to reduce the inappropriate use of antipsychotics.
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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 | 0.019 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".