A global perspective of Indigenous child health research: a systematic review of longitudinal studies
Bibliographic record
Abstract
BACKGROUND: Rigorously designed longitudinal studies can inform how best to reduce the widening health gap between Indigenous and non-Indigenous children. METHODS: A systematic review was performed to identify and present the breadth and depth of longitudinal studies reporting the health and well-being of Indigenous children (aged 0-18 years) globally. Databases were searched up to 23 June 2020. Study characteristics were mapped according to domains of the life course model of health. Risk of bias was assessed using the National Institutes of Health (NIH) Study Quality Assessment Tools. Reported level of Indigenous involvement was also appraised; PROSPERO registration CRD42018089950. RESULTS: From 5545 citations, 380 eligible papers were included for analysis, representing 210 individual studies. Of these, 41% were located in Australia (n = 88), 22.8% in the USA (n = 42), 11.9% in Canada (n = 25) and 10.9% in New Zealand (n = 23). Research tended to focus on either health outcomes (50.9%) or health-risk exposures (43.8%); 55% of studies were graded as 'good' quality; and 89% of studies made at least one reference to the involvement of Indigenous peoples over the course of their research. CONCLUSIONS: We identified gaps in the longitudinal assessment of cultural factors influencing Indigenous child health at the macrosocial level, including connection to culture and country, intergenerational trauma, and racism or discrimination. Future longitudinal research needs to be conducted with strong Indigenous leadership and participation including holistic concepts of health. This is critical if we are to better understand the systematic factors driving health inequities experienced by Indigenous children globally.
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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 | 0.071 | 0.191 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.021 | 0.021 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".