Inter-sectoral collaboration in the context of supporting adults with intellectual and developmental disabilities who are frail
Bibliographic record
Abstract
There has been a steady growth in the population of adults with intellectual and developmental disabilities, some of whom are known to age prematurely; aging is also associated with increased rates of disability and chronic conditions that can lead to frailty. Supports provided by social and health sectors are crucial to supporting those identified as frail. A case study design was used to investigate the implementation of inter-sectoral collaboration between providers of services from specific social and health sectors; namely, developmental services (aka disability services) and home care services, in the context of supporting those identified as frail. Twenty-three participants (including individuals with intellectual and developmental disabilities, family members, and providers from both sectors) were interviewed using an open-ended format targeting known conditions for effective inter-sectoral collaboration: necessity, opportunity, capacity, relationships, planned action, and sustained outcomes. Interviews were recorded, transcribed verbatim, and coded by two independent researchers. All participants touched on key facilitators and barriers for successful inter-sectoral collaboration across the six conditions. A single exception occurred in that individuals and families did not discuss sustained outcomes. Each of the six conditions for effective inter-sectoral collaboration is relevant to planning required to support adults with intellectual and developmental disabilities who are frail. When it comes to collaborative ventures between social and healthcare teams, the use of resources and tools that both facilitate and promote these conditions should be prioritised.
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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 | 0.022 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.002 |
| 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".