Health System Enablers and Barriers to Continuity of Care for First Nations Peoples Living with Chronic Disease
Bibliographic record
Abstract
Introduction: Failings in providing continuity of care following an acute event for a chronic disease contribute to care inequities for First Nations Peoples in Australia, Canada, and Aotearoa (New Zealand). Methods: A rapid narrative review, including primary studies published in English from Medline, Embase, PsycINFO, and Cochrane Central, concerning chronic diseases (cancer, cardiovascular disease, chronic kidney disease, diabetes, and related complications), was conducted. Barriers and enablers to continuity of care for First Nations Peoples were explored considering an empirical lens from the World Health Organization framework on integrated person-centred health services. Results: Barriers included a need for more community initiatives, health and social care networks, and coaching and peer support. Enabling strategies included care adapted to patients' cultural beliefs and behavioural, personal, and family influences; continued and trusting relationships among providers, patients, and caregivers; and provision of flexible, consistent, adaptable care along the continuum. Discussion: The support and co-creation of care solutions must be a dialogical participatory process adapted to each community. Conclusions: Health and social care should be harmonised with First Nations Peoples' cultural beliefs and family influences. Sustainable strategies require a co-design commitment for well-funded flexible care plans considering coaching and peer support across the lifespan.
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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 | 0.013 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".