Bibliographic record
Abstract
This article presents findings of a collaborative, community-based project that established partnerships between researchers at Simon Fraser University and Indigenous community members and organizations in Surrey, British Columbia. In Metro Vancouver, Surrey saw the biggest increase in its Indigenous population, which grew 77 per cent between 2006 and 2016 to 13,460. This was a timely project given that community research partners were interested in focusing on health issues and that traditional health and social service models geared towards Indigenous populations tend to focus on on-reserve populations. Research objectives included: 1) to identify the health needs and priorities of Indigenous peoples in Surrey; 2) to determine what methodologies should be used when researching culture and health; and 3) to explore how existing data connected to Surrey’s Indigenous population can be accessed to better align research priorities with the health status of Indigenous peoples in Surrey. Data was gathered through two community talking circles (n=30) and one-to-one interviews with health and social service providers (n=12). Findings from the project included: the importance of culturally safe care and support in health and social service systems; the need for training and education among health and social service providers on the history and contemporary experiences of Indigenous peoples; and the importance of having access to Elder-led cultural teachings and land-based activities that support health and wellbeing of families and communities.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.337 | 0.069 |
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".