Training Needs of Manitoba Pharmacists to Increase Application of Assessment and Prescribing for Minor Ailments into Practice: A Qualitative and Quantitative Survey
Bibliographic record
Abstract
Current literature demonstrates the positive impact of pharmacists prescribing medication on patient outcomes and pharmacist perceptions of the practice. The aim of this study was to understand the factors affecting prescribing practices among Manitoba pharmacists and identify whether additional training methods would be beneficial for a practice behavior change. A web-based survey was developed and participation was solicited from pharmacists in Manitoba. Descriptive statistics were calculated to summarize the frequency of demographic characteristics. Chi-square tests were used to explore possible correlations between variables of interest and thematic analysis of qualitative data was completed. A total of 162 participants completed the survey. The response rate was 12.3%. Of those who had met the requirements to prescribe, none were doing so on a daily basis and 23.5% had not assessed or prescribed since being certified. Respondents identified the top barriers for providing this service as a lack of sufficient revenue and a lack of time. Qualitative analysis of responses identified additional barriers including a limiting scope and inadequate tools. Approximately half (54.4%) of respondents expressed that additional training would be of value. The themes identified from the survey data suggest that practice-based education would help pharmacists apply skills. In addition, expansion of prescribing authority and strategies addressing remuneration issues may help overcome barriers to pharmacists prescribing within Manitoba.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".