The role of Indigenous Health Workers in ear health screening programs for Indigenous children: a scoping review
Bibliographic record
Abstract
OBJECTIVE: To identify and describe the involvement of Indigenous Health Workers within ear health screening programs for Indigenous Peoples in Australia, Canada, the US and New Zealand. METHODS: Peer-reviewed and grey literature sources were systematically searched to identify evidence. This scoping review was conducted in accordance with the scoping review extension of the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. RESULTS: Forty pieces of evidence were included in this review. While almost all included studies identified the critical role of Indigenous Health Workers in ear and hearing health, Indigenous leadership and involvement in research projects and service delivery varied significantly and none of the included studies reported Indigenous health worker perspectives. Approximately half of the authorship teams had at least one Indigenous author. CONCLUSIONS: There is a clear need for Indigenous leadership in ear and hearing health research and programming. Specialist teams involved in health service delivery and research need to enable this transition by understanding and privileging Indigenous leadership and investing in appropriate training for non-Indigenous specialists providing care in Indigenous health contexts. IMPLICATIONS FOR PUBLIC HEALTH: These findings are discussed in terms of opportunities to improve Indigenous ear and hearing health research and programming.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".