Improving cultural competence of healthcare workers in First Nations communities: a narrative review of implemented educational interventions in 2015–20
Bibliographic record
Abstract
BACKGROUND: Cultural competency is often promoted as a strategy to address health inequities; however, there is little evidence linking cultural competency with improved patient outcomes. This article describes the characteristics of recent educational interventions designed to improve cultural competency in healthcare workers for First Nations peoples of Australia, New Zealand, Canada and the USA. METHODS: In total, 13 electronic databases and 14 websites for the period from January 2015 to May 2021 were searched. Information on the characteristics and methodological quality of included studies was extracted using standardised assessment tools. RESULTS: Thirteen published evaluations were identified; 10 for Australian Aboriginal and Torres Strait Islander peoples. The main positive outcomes reported were improvements in health professionals' attitudes and knowledge, and improved confidence in working with First Nations patients. The methodological quality of evaluations and the reporting of methodological criteria were moderate. CONCLUSIONS: Cultural competency education programs can improve knowledge, attitudes and confidence of healthcare workers to improve the health of First Nations peoples. Providing culturally safe health care should be routine practice, particularly in places where there are concentrations of First Nations peoples, yet there is relatively little research in this area. There remains limited evidence of the effectiveness of cultural education programs alone on community or patient outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".