Best Practices to Support the Self-Determination of Indigenous Communities, Collectives, and Organizations in Health Research through a Provincial Health Research Network Environment in British Columbia, Canada
Bibliographic record
Abstract
In Canada, the health research funding landscape limits the self-determination of Indigenous peoples in multiple ways, including institutional eligibility, priority setting, and institutional structures that deprioritize Indigenous knowledges. However, Indigenous-led research networks represent a promising approach to transforming the funding landscape to better support the self-determination of Indigenous peoples in health research. The British Columbia Network Environment for Indigenous Health Research (BC NEIHR) is one of nine Indigenous-led networks across Canada that supports research leadership among Indigenous (First Nations, Métis, and Inuit) communities, collectives, and organizations (ICCOs). In this paper, we share three best practices to support the self-determination of ICCOs in health research based on three years of operating the BC NEIHR: (1) creating capacity-bridging initiatives to overcome funding barriers; (2) building relational research relationships with ICCOs ("people on the ground"); and (3) establishing a network of partnerships and collaborations to support ICCO self-determination. Supporting the self-determination of ICCOs and enabling them to lead their own health research is a critical pathway toward transforming the way Indigenous health research is funded and conducted in Canada.
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 | 0.072 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.043 | 0.015 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.006 | 0.022 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".