Pharmacist provision of primary health care: a modified Delphi validation of pharmacists' competencies
Bibliographic record
Abstract
BACKGROUND: Pharmacists have expanded their roles and responsibilities as a result of primary health care reform. There is currently no consensus on the core competencies for pharmacists working in these evolving practices. The aim of this study was to develop and validate competencies for pharmacists' effective performance in these roles, and in so doing, document the perceived contribution of pharmacists providing collaborative primary health care services. METHODS: Using a modified Delphi process including assessing perception of the frequency and criticality of performing tasks, we validated competencies important to primary health care pharmacists practising across Canada. RESULTS: Ten key informants contributed to competency drafting; thirty-three expert pharmacists replied to a second round survey. The final primary health care pharmacist competencies consisted of 34 elements and 153 sub-elements organized in seven CanMeds-based domains. Highest importance rankings were allocated to the domains of care provider and professional, followed by communicator and collaborator, with the lower importance rankings relatively equally distributed across the manager, advocate and scholar domains. CONCLUSIONS: Expert pharmacists working in primary health care estimated their most important responsibilities to be related to direct patient care. Competencies that underlie and are required for successful fulfillment of these patient care responsibilities, such as those related to communication, collaboration and professionalism were also highly ranked. These ranked competencies can be used to help pharmacists understand their potential roles in these evolving practices, to help other health care professionals learn about pharmacists' contributions to primary health care, to establish standards and performance indicators, and to prioritize supports and education to maximize effectiveness in this role.
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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 | 0.063 | 0.059 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| 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".