Health workforce cultural competency interventions: a systematic scoping review
Bibliographic record
Abstract
BACKGROUND: Addressing health workforce cultural competence is a common approach to improving health service quality for culturally and ethnically diverse groups. Research evidence in this area is primarily focused on cultural competency training and its effects on practitioners' knowledge, attitudes, skills and behaviour. While improvements in measures of healthcare practitioner cultural competency and other healthcare outcomes have been reported, there are concerns around evidence strength and quality. This scoping review reports on the intervention strategies, outcomes, and measures of included studies with the purpose of informing the implementation and evaluation of future interventions to improve health workforce cultural competence. METHODS: This systematic scoping review was completed as part of a larger systematic literature search conducted on cultural competence intervention evaluations in health care in Canada, the United States, Australia and New Zealand published from 2006 to 2015. Overall, 64 studies on cultural competency interventions were found, with 16 aimed directly at the health workforce. RESULTS: There was significant heterogeneity in workforce intervention strategies, measures and outcomes reported across studies making comparisons of intervention effects difficult. The two main workforce intervention strategies identified were cultural competency training and other professional development interventions including other training and mentoring. Positive outcomes were commonly reported for improved practitioner knowledge (9/16), skills (7/16), and attitudes/beliefs (5/16). Although health care (6/16) and health (2/16) outcomes were reported in some studies there was very limited evidence of positive intervention impacts. Only four studies utilised existing validated measurement tools to assess intervention outcomes. CONCLUSION: Training and development of the health workforce remain a principle strategy towards the goal of improved cultural competence in health services and systems. Diverse approaches are available to increase health workforce cultural competence. However, the effects of interventions beyond practitioner knowledge and attitudes remains unclear. Assessment of practitioner behavioural outcomes as well as measures of intervention impact on healthcare and health outcomes are needed to build a stronger evidence base.
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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 | 0.026 | 0.109 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.017 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".