Co‐creating better healthcare experiences for First Nations children and youth: The FIRST approach emerges from Two‐Eyed <scp>s</scp>eeing
Bibliographic record
Abstract
To achieve health, Indigenous people seek a life that balances mental, spiritual, emotional, and physical wellness, yet the scope of these four dimensions is not typically considered in the Western-based health system. Indigenous people experience ongoing pain and hurt in all these dimensions as a result of a colonial legacy that persists in current-day policy and care contexts. Exploring ways to support Indigenous people to embrace ways of being well and reducing chronic pain has not been a priority area in health research. This community-based, qualitative study in four First Nations communities involved conversation sessions with 188 First Nations children, youth, parents, and Elders and 32 professionals who practice in those communities. The purpose was to gather perspectives related to pain expression, care experiences, and the strategies to improve the healthcare encounter. Thematic analysis was used to identify a more culturally thoughtful approach for clinicians to consider when First Nations people seek care. Two-Eyed Seeing consisting of four iterative steps was used to co-create the FIRST approach validating for community members that their perspectives were heard and providing a clinical approach for culturally safe practices with children, youth, and families. An overarching theme in the results was a clearer understanding about how pain and hurt translate into participants' health experiences and their desire to have their knowledge reflected in their health care. Participants describe experiencing pain and hurt in all four dimensions of health and from a historical, cultural, and spiritual identity, as well as from a community, family, and individual perspective. The FIRST approach captures Indigenous knowledge relating to Family, Information, Relationship, Safe-Space, and Two-Eyed treatment in the healthcare encounter. Considerations of this approach in clinical practice could enhance respectful and trusting relationships, knowledge exchange for better care experiences, and potentially improvement of culturally sensitive outcomes for Indigenous people.
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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.021 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".