Evaluation of a continuous quality improvement program in anticoagulant therapy
Bibliographic record
Abstract
BACKGROUND: The ACO Program (Programme ACO), a continuous quality improvement program (CQIP) in anticoagulation therapy, was offered in community pharmacies as a pilot project. OBJECTIVE: To evaluate the participants' appreciation for the various activities of the program. METHODS: Participants had access to training activities, including an audit with feedback, online training activities (OTA), clinical tools and support from facilitators. Cognitive behavioural learning determinants were evaluated before and 5 months after the beginning of the program. Participants' satisfaction and perception were documented via online questionnaires and a semistructured interview. RESULTS: Of the 52 pharmacists in the ACO Program, 47 participated in this evaluation. Seventy-seven percent of the participants completed at least 1 OTA and 6% published on the forum. The feeling of personal effectiveness rose from 8.01 (7.67-8.35) to 8.62 (8.24-8.99). The audit and feedback, as well as the high-quality OTA and their lecturers, were the most appreciated elements. DISCUSSION: There was a high OTA participation rate. The facilitators seemed to play a key role in the CQIP. The low level of participation in the forum reflects the known phenomenon of social loafing. Technical difficulties affecting the platform and data collection for the audit with feedback constituted limitations. CONCLUSION: The CQIP in anticoagulation therapy is appreciated by community pharmacists and is associated with an improved feeling of personal effectiveness.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".