Patterns of Mental Healthcare Provision in Urban Areas: A Comparative Analysis for Informing Local Policy
Bibliographic record
Abstract
Abstract ObjectiveUrbanisation presents specific challenges for the mental wellbeing of the population. An understanding of availability of existing service provision in urban areas is necessary to plan for the needs of people with mental illness in these contexts to identify gaps in care provision and inform policy and planning. This study aims to provide an analysis of the availability and diversity of mental health care in urban areas in Australia , and compare it with benchmark areas in Europe (Finland and Spain) and South America (Chile). MethodDESDE-LTC, an instrument for service classification and description of services providing long term care was used to analyse and compare service provision in Australia (Australian Capital Territory (ACT)), to other urban areas in Australia (Western Sydney, Perth North and South East Sydney) and to benchmark areas in other countries (Spain, Finland and Chile), using a standard healthcare ecosystems approach. Services from all relevant care sectors were calculated per 100,000 adults.ResultsWe identified commonalities in the pattern of mental health care in urban regions in Australia when compared to urban regions internationally, as well as gaps in care provision common to all study areas.ConclusionThese results highlight the relevance of an ecosystems approach to service planning in mental health care at the local level, and the use of a standardised instrument able to provide valid comparisons. There is a need for models of care sensitive to mental health care ecosystem indicators.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".