Propelled by the Pandemic: Responses and Shifts in Primary Healthcare Models for Indigenous Peoples
Bibliographic record
Abstract
The COVID-19 pandemic posed a significant risk to the health and well-being of First Nations and Métis communities in Alberta.Communities' self-determined and integrated responses with embedded cultural supports -in collaboration with governments, organizations and providers -were key to minimizing morbidity and mortality.Maintaining and building these relationships in the continued pandemic response, broadening approaches to healthcare delivery and continuing to include culture will support attainment of the Indigenous primary healthcare model while addressing logistical challenges in transforming and sustaining healthcare systems in the background of ongoing inequities in the social determinants of health. RésuméLa pandémie de la COVID-19 a posé un risque important pour la santé et le bien-être des communautés des Premières Nations et des Métis en Alberta.La réaction autodéterminée et intégrée des communautés avec le soutien culturel intégré -en collaboration avec les gouvernements, les organisations et les prestataires -a été essentielle pour minimiser la morbidité et la mortalité.L'établissement et le maintien de ces relations dans la réaction face à la pandémie, l'élargissement des approches pour la prestation des soins de santé et l'inclusion des éléments culturels permettront d' atteindre un modèle autochtone en matière de soins de santé primaires, tout en relevant les défis logistiques liés à la transformation et au maintien des systèmes de santé dans le contexte des inégalités qui persistent dans les déterminants sociaux de la santé. Responses and Shifts in Primary Healthcare Models for Indigenous Peoples Avoid burnout
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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 | 0.012 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".