Closing the health service gap: Métis women and solutions for culturally-safe health services
Bibliographic record
Abstract
Metis peoples, while comprising over a third of the total Indigenous population in Canada, experience major gaps in health services that are culturally-safe. This is problematic given Metis peoples experience severe disparities in health determinants and outcomes compared to the non-Indigenous Canadian population. At the same time, Metis are unlikely to engage in health services that do not value their cultural identities, often utilising mainstream options. Traditionally, Metis women were central to the health and well-being of their communities. However, due to decades of colonial legislation and land displacement, female narratives have been silenced, and Metis identities have been fractured. This has resulted in having direct implications on Metis peoples current health and access to health services. Solutions to filling the Metis health service gap may lie in the all too often unacknowledged or missing voices of Metis women. Given these contexts, this commentary aims to generate critical discussion on the culturally-safe health care gap for Metis peoples in Canada. It does this by calling on policymakers, health care workers, and researchers alike to engage with Metis women regarding the health of Metis communities, and finding solutions towards identifying and implementing pathways to culturally-safe healthcare.
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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.032 | 0.032 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.006 | 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".