Patterns of Mental Health Care in Remote Areas: Kimberley (Australia), Nunavik (Canada), and Lapland (Finland): Modèles de soins de santé mentale dans les régions éloignées: Kimberley (Australie), Nunavik (Canada) et Laponie (Finlande)
Bibliographic record
Abstract
OBJECTIVE: Mental health (MH) care in remote areas is frequently scarce and fragmented and difficult to compare objectively with other areas even in the same country. This study aimed to analyze the adult MH service provision in 3 remote areas of Organization for Economic Cooperation and Development countries in the world. METHODS: We used an internationally agreed set of systems indicators, terminology, and classification of services (Description and Evaluation of Services and DirectoriEs for Long Term Care). This instrument provided a standard description of MH care provision in the Kimberley region (Australia), Nunavik (Canada), and Lapland (Finland), areas characterized by an extremely low population density and high relative rates of Indigenous peoples. RESULTS: All areas showed high rates of deprivation within their national contexts. MH services were mostly provided by the public sector supplemented by nonprofit organizations. This study found a higher provision per inhabitant of community residential care in Nunavik in relation to the other areas; higher provision of community outreach services in the Kimberley; and a lack of day services except in Lapland. Specific cultural-based services for the Indigenous population were identified only in the Kimberley. MH care in Lapland was self-sufficient, and its care pattern was similar to other Finnish areas, while the Kimberley and Nunavik differed from the standard pattern of care in their respective countries and relied partly on services located outside their boundaries for treating severe cases. CONCLUSION: We found common challenges in these remote areas but a huge diversity in the patterns of MH care. The implementation of care interventions should be locally tailored considering both the environmental characteristics and the existing pattern of service provision.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".