Examining the Role of Indigenous Primary Healthcare in Addressing the Social Determinants of Health During a Public Health Crisis
Bibliographic record
Abstract
Primary healthcare (PHC) is the foundation of health and wellbeing at every stage of life. In the midst of a public health crisis or emergency response, we cannot afford to ignore people’s broader health needs – including reproductive, maternal and child health care; mental health; and treatment for chronic diseases. Research evidence shows that when health systems are overwhelmed during outbreaks, deaths caused by lapses in routine care can increase dramatically. Indigenous PHC services arose in many countries (e.g., Canada, Australia, New Zealand, U.S) from the inability of mainstream health services to adequately meet the needs of Indigenous communities. By Indigenous PHC services, we are referring to services designed and controlled by local communities and underpinned by the values and principles of the communities. Indigenous PHC can include a range of comprehensive programs that incorporate treatment and management, prevention and health promotion, as well as addressing the social determinants of health (SDOH) and a focus on redressing health inequities. Therefore, given the important role of Indigenous PHC we will examine how Indigenous PHC mobilizes during a public health crisis to address patient needs, including the broader SDOH, as well as innovative approaches to Indigenous PHC delivery (e.g., service adaptations, new organizational models of service delivery) that emerge during a public health crisis in order to respond to patient’s health needs. This rapid review will address two questions: 1. How does Indigenous primary healthcare (PHC) mobilize to address patient needs during a public health crisis and what are the components (organization, systems, programs, providers, communities)? 2. What do we know about innovative approaches to Indigenous primary health care (PHC) that can inform service delivery and meet the needs of Indigenous peoples during a public health crisis?
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".