Working well: a systematic scoping review of the Indigenous primary healthcare workforce development literature
Bibliographic record
Abstract
Background: Strong and effective workforce models are essential for improving comprehensive Indigenous primary healthcare service (PHC) provision to Indigenous peoples in Canada, Australia, New Zealand and the USA (CANZUS nations). This review systematically scoped the literature for studies that described or evaluated models and systems that support the sustainability, capacity or growth of the Indigenous PHC workforce to provide effective PHC provision. Methods: Eleven databases, 10 websites and clearinghouses, and the reference lists of 5 review articles were searched for relevant studies from CANZUS nations published in English from 2000 to 2017. A process of thematic analysis was utilised to identify key conditions, strategies and outcomes of Indigenous PHC workforce development reported in the literature. Results: Overall, 28 studies were found. Studies reported enabling conditions for workforce development as government funding and appropriate regulation, support and advocacy by professional organisations; community engagement; PHC leadership, supervision and support; and practitioner Indigeneity, motivation, power equality and wellbeing. Strategies focused on enhancing recruitment and retention; strengthening roles, capacity and teamwork; and improving supervision, mentoring and support. Only 12/28 studies were evaluations, and these studies were generally of weak quality. These studies reported impacts of improved workforce sustainability, workforce capacity, resourcing/growth and healthcare performance improvements. Conclusions: PHCs can strengthen their workforce models by bringing together healthcare providers to consider how these strategies and enabling conditions can be improved to meet the healthcare and health needs of the local community. Improvement is also needed in the quality of evidence relating to particular strategies to guide practice.
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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 | 0.029 | 0.074 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.029 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| 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".