Reporting on knowledge, attitudes, and behaviours of pharmacists regarding the active offer of French language health services in Ontario: A quantitative survey study
Bibliographic record
Abstract
Background: Patient-provider language discordances can negatively affect the availability, accessibility, acceptability, and quality (AAAQ) of pharmacy services as described by the AAAQ framework. With nearly 600,000 residents of Ontario identifying French as their mother tongue, the active offer (AO) of French language pharmacy services should be provided, yet little is known about pharmacists' familiarity and use of AO. Methods: Ontario pharmacists completed an online survey measuring their knowledge, attitudes, and behaviours (KAB) regarding the AO for French language services (FLS). Demographics, including ability to converse in French, were collected. KAB AO scores were calculated and compared using descriptive and inferential statistics. Results: A total of 360 pharmacists completed the survey. The majority (65.1%) practiced in a primary care setting, 21.1% spoke French, and 71.8% lived in an area where less than 10% of the population were Francophone. Overall, AO scores were highest for attitude (3.8/5) and lowest for behaviours (2/5) and knowledge (1.8/5). Most pharmacists (62.8%) indicated an interest in AO training. Discussion: Gaps in KAB regarding AO for FLS by Ontario pharmacists resemble those observed throughout the health care system. Further training is suggested to address the lack of knowledge regarding AO, particularly its proactive nature, as well as strategies for its implementation. Conclusion: Although most participating pharmacists demonstrated gaps in AO knowledge and behaviour, many had favourable attitudes toward the importance of AO in the pharmacy setting and agreed that further training would be needed to improve their KAB. Further, the AO can address all 4 elements of the AAAQ framework.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".