Moving towards increased cultural competency in public health research
Bibliographic record
Abstract
There has been a renewed focus in recent decades on collaborative approaches in community-based public health research and interventions. This is an important grounding for addressing the needs of culturally and linguistically diverse (CALD) communities. But how well do we as researchers prepare for the complexities of working with CALD communities? And what sort of support do we need to meet the challenges of the task? Cultural competence refers to the extent to which researchers, practitioners and organisations have the necessary skills, knowledge, attitudes and policies to work effectively in cross-cultural situations. The shift towards cultural competence in public health is evidenced by the development of policies and guidelines by government bodies and leading research institutions in countries such as Canada, the United States, Australia and New Zealand. This chapter will draw on these guidelines, on models of cultural competency used in welfare and health service delivery, and on collaborative research approaches. A framework for moving towards cultural competence in public health research and health promotion interventions will be discussed, drawing case study examples from the co-authors' community-based experiences. This will highlight the complexities but also the importance of adopting culturally competent strategies in public health research and health promotion interventions. The need for supporting government and funding structures will also be proposed .
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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 | 0.367 | 0.253 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.010 | 0.061 |
| Scholarly communication | 0.023 | 0.023 |
| Open science | 0.005 | 0.045 |
| Research integrity | 0.009 | 0.020 |
| Insufficient payload (model declined to judge) | 0.003 | 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".