Pain management in indigenous and tribal peoples: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Effective and culturally safe pain management can facilitate analgesia and improve the quality of life. Individualised, multimodal and multidisciplinary approaches are highly recommended. There exist gaps in the knowledge on pain management, in terms of the assessment and/or treatment, in indigenous peoples and the currently available information is scattered in the literature. A scoping review will provide an overview or evidence map on the variety of approaches used in different cultures, in different parts of the world. METHODS AND ANALYSIS: The search strategy comprises three stages. The first stage identified the MeSH terms and keywords in PubMed. The second stage will consist of a search of MEDLINE, EMBASE, LILACS, CINAHL, Web of Science, APA PsycNet and Scopus, followed by a search in Google and Google Scholar, GreyGuide, ProQuest Dissertations and Theses, Theses Canada Portal (Library and Archives Canada), TROVE (National Library of Australia), Aboriginal and Torres Strait Islander Health Bibliography, and Cybertesis. The papers will be screened, selected and extracted independently by two researchers. Descriptive data analysis will be performed, and the results will be presented using a narrative summary, graphs, tables and figures. ETHICS AND DISSEMINATION: This review does not require ethical approval, as data from the literature available in databases will be collected and analysed. The protocol was registered at the Open Science Framework. The data on pain assessment and treatment in indigenous peoples will be presented through a narrative summary, figures, charts and tables. Results will be submitted to an open-access journal for publication and will be disseminated through scientific events, scientific meetings, public events and conversation circles with indigenous peoples.
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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 | 0.117 | 0.082 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.009 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.011 | 0.007 |
| Insufficient payload (model declined to judge) | 0.080 | 0.019 |
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".