Canada First Nations Strengths in Community-Based Primary Healthcare
Bibliographic record
Abstract
INTRODUCTION: First Nation (FN) peoples and communities in Canada are still grappling with the effects of colonization. Health and social inequities result in higher disease burden and significant disparities in healthcare access and responsiveness. For resilience, survival, and self-determination, FN are looking inwards for strengths. This paper reports on the cultural, community, and family strengths that have supported FN communities in developing community-based primary healthcare (CBPHC) strategies to support health and wellbeing. METHODS: The study was a partnership between university-based researchers; The First Nations Health and Social Secretariat of Manitoba; and eight First Nation communities in Manitoba. Community-based participatory research methods were used to engage the participating communities. One hundred and eighty-three in-depth, semi-structured key informant interviews were completed between 2014 and 2016 with key members of the First Nation communities, i.e., community-based health providers and users of primary healthcare services, representing all age and genders. Data-collection and analysis were conducted following iterative grounded theory analysis. RESULTS: Community-based healthcare models based on local strengths support easier access and shorter wait times for care and compassionate care delivery. Resources such as homecare and medical transportation are helpful. Community cooperation, youth power, responsive leadership, and economic development as well as a strong cultural and spiritual base are key strengths supporting health and social wellbeing. CONCLUSIONS: Locally led, self-determined care adds strength in FN communities, and is poised to create long-lasting primary healthcare transformation.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.018 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".