Establishing a comprehensive search strategy for Indigenous health literature reviews
Bibliographic record
Abstract
BACKGROUND: Appropriate search strategies are essential to ensure the integrity and reproducibility of systematic and scoping reviews, as researchers seek to capture as many relevant resources as possible. In the case of Indigenous health reviews, researchers are met with the special challenge of creating a search strategy that can encompass this large, diverse population group with no universally agreed upon identification criteria. MAIN BODY: With an aim to promote improved review methodologies that uphold standards of justice, autonomy, and equity for Indigenous peoples and other heterogeneous populations, we describe critical gaps and approaches to close them. We report organizational and transparency issues around how Indigenous populations are indexed in several major databases, and draw on examples of published reviews and protocols to demonstrate the challenges inherent to creating a comprehensive search strategy. CONCLUSIONS: The conduct and communication of results from health literature research on global Indigenous populations are compromised by challenges of methodology that are rooted in the complexities inherent to defining Indigenous peoples. These challenges must be urgently addressed to improve this important field of inquiry moving forward.
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 | 0.518 | 0.726 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.014 | 0.011 |
| Bibliometrics | 0.063 | 0.041 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.009 | 0.022 |
| Research integrity | 0.021 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.007 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".