Pharmacy practice and First Peoples health equity: A scoping review
Bibliographic record
Abstract
BACKGROUND: First Peoples health inequity is observed globally in higher rates of chronic disease compared to non-First Peoples. Pharmacy practice is an essential component of chronic disease management; achieving a good health-related quality of life and the best clinical outcomes requires optimal pharmaceutical care. AIM: To identify pharmacy practice strategies and interventions, across the globe, contributing to achieving First Peoples health equity; including reported outcomes, impact, implementation barriers/enablers and identification of practice gaps. METHOD: PRISMA-ScR followed for reporting and review protocol is published. Inclusion criteria comprised First Peoples, reported strategies and/or interventions aligned to international conceptual model for pharmaceutical practice, study motive to achieve First Peoples equitable healthcare. Included articles mapped to a contemporary framework and underwent inductive content analysis. FINDINGS: Thirty-six studies were reviewed from Australia (39 %), the United States of America (36 %), New Zealand (17 %), Canada (5 %) and Brazil (3 %). Three main strategies emerged, clinical pharmacy practice, medicines access and managing medicines. Advanced pharmacy practice improved clinical outcomes with significant decreases in specific chronic disease target indicators along with reductions in emergency department visits and hospitalizations reported; social needs screening and referral highlighted as a major gap. CONCLUSION: Advanced pharmacy practice models of care are urgently required to maximize the pharmacy professions contribution to achieving First Peoples health equity. Culturally appropriate, innovative, flexible models incorporating social requirements will generate the greatest impact. Pharmacists require high level communication/leadership skills and an understanding of First Peoples health determinants to build authentic patient-practitioner partnerships, increase community engagement and lead transformative change.
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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 | 0.013 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".