Systematic scoping review of occupational health injuries and illnesses among Indigenous workers
Bibliographic record
Abstract
Indigenous populations in the USA, Australia, New Zealand (NZ) and Canada total more than 13 million, but continue to be marginalised in their respective regions. The goal of this comprehensive review of all studies evaluating adverse occupational health outcomes among Indigenous populations in these countries was to identify gaps in the literature and future research directions. A systematic scoping review of research published between 1970 and 2020 was undertaken using the methodological framework initially proposed by Arksey and O'Malley. Country, Indigenous participants, study type, exposure, adverse health outcome, occupation and industry were identified for each paper. Of the 1272 research papers identified, only 51 articles met the inclusion criteria of this scoping review. Almost half of the studies (n=24, 47.1%) were published after 2010. Only 13 (25.5%) studies specifically focused on Indigenous persons at the time of the study design, and less than half of the studies (47.1%) included more than 100 Indigenous participants. Most studies used the following general terms without mention of specific indigenous groups: Indigenous (Australia), Māori (NZ), Aboriginal (Canada) and American Indian or Alaskan Native (USA). Only one study acknowledged asking respondents their preferred terminology. Over the past 50 years, there has been a paucity of research directly or indirectly evaluating occupational health outcomes of Indigenous populations in these four countries. There is a need for better sampling strategies and inclusion of demographic questions that capture Indigenous status in surveys, workers' compensation data and other commonly used data sources to develop adequate baseline data for targeted future interventions.
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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 | 0.019 | 0.072 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.019 | 0.017 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| 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".