Needs Assessment for Pharmacy Program Creation Designed to Serve Minority Francophone Populations in Canada
Bibliographic record
Abstract
OBJECTIVES: Current literature provides little insight into the need for French-language pharmaceutical services in Francophone minority settings in Canada. This study aims to understand the pharmaceutical care and services offered in French in Canada. It also aims to conduct a needs assessment in the context of curriculum development, by validating whether pharmaceutical needs are being met in Francophone minority settings in Canada. METHODS: An online survey was sent to community members and health care professionals. Respondents were asked to identify the perceived importance of pharmaceutical needs and the degree to which they perceive these to be fulfilled in French and English in their communities. RESULTS: A total of 113 community members and 109 health care professionals completed the survey. Most respondents were from Ontario (64.84%), Quebec (10.50%), or Atlantic Provinces (10.05%). In total, > 95% of survey respondents identified that pharmaceutical needs assessed were of very high importance. The rate of pharmaceutical need fulfillment was lower in French than English across all pharmaceutical needs assessed. The greatest difference in rate of pharmaceutical need fulfillment was seen with "Having safe access to required medication". The perception of pharmaceutical needs being met was congruent between community members and health care professionals. CONCLUSION: These results confirm a lack of pharmaceutical needs being met in French in Canadian Francophone minority communities. There is a lack of French-language services that limit the ability to receive care in one's own language. Pharmacy education in French may be an effective approach to improve pharmaceutical care services received in French in Francophone minority communities.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".