Landscape of Métis health and wellness: protocol for a scoping review
Bibliographic record
Abstract
INTRODUCTION: . In line with Métis people having a unique culture, history, language and way of life, a distinctions-based approach is critical to understand the current landscape of Métis-specific health. In this paper, we present a scoping review protocol to describe this research landscape in Canada led by the Métis Nation of Ontario (MNO). METHODS AND ANALYSIS: This scoping review protocol is reported in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews reporting guidelines and follows Arksey and O'Malley's scoping review methodology. We will search electronic databases (Scopus, MEDLINE, Embase, Web of Science, CINAHL, APA PsycINFO, Anthropology Plus, Bibliography of Indigenous Peoples of North America, Canadian Business and Current Affairs, Indigenous Studies Portal, Informit Indigenous Collection, Collaborative Indigenous Garden, PubMed, ProQuest), grey literature sources and reference lists from selected papers. Two reviewers (HMB and SK) will double-blind screen all titles/abstracts and full-text studies for inclusion. Any health-related study or health report that includes a Métis-specific health, well-being or Métis social determinant of health outcome will be included. Relevant variables will be extracted following an iterative process whereby the data charting will be reviewed and updated. ETHICS AND DISSEMINATION: Findings from this scoping review will be shared back through the MNO's existing community-based communication channels. Traditional academic dissemination will also be pursued. Research ethics board approval is not required, since data are from peer-reviewed publications or publicly shared health reports and knowledge translation products.
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 | 0.166 | 0.212 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.009 | 0.010 |
| Bibliometrics | 0.017 | 0.018 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.140 | 0.028 |
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".