Integrated health and social care with a CALD focus: a service mapping study in Sydney Metropolitan
Bibliographic record
Abstract
Background: Underrepresented and underserved communities such as culturally and linguistically diverse (CALD) groups experience higher reported prevalence and severity of multimorbidity along with unmet social needs and disadvantages in accessing services. Minority populations report higher rates of emergency department presentations globally and nationally and it is understood that services are more frequently accessed on consumer initiative when problems become acute and critical, demonstrating significant barriers for early intervention. In Australia, most of the efforts to reduce over-utilisation of emergency care have focused on developing integrated health and social care approaches for the general population. There is little evidence available on how CALD populations are involved in the design and implementation of integrated care health and social care initiatives and how their needs are identified and addressed. Methods: A service mapping study informing the development of a framework for comparative analysis of integrated health and social care services targeting CALD populations will be sought. Semi-structured interviews, with key stakeholders and decision-makers representing health and social services, as well as document review, will inform the development of a map of integrated health and social care services available for individuals from culturally and linguistically diverse backgrounds. Core defining elements of the mapped services comprise the basis for the framework that enables comparison of level of integration with a CALD focus, including governance and partnerships, health and social care staff, financing and payment systems, and data sharing and use. Implications: This study will advance the understanding of how integrated health and social care policies (policy learning) and services are conceptualised, developed, and implemented at a local level (policy implementation) to address the needs of CALD populations. and how they can better respond to the health and social needs of CALD consumers.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| 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".