Critical success factors for school‐based integrated health care models: Learnings from an Australian example
Bibliographic record
Abstract
ISSUES ADDRESSED: Integrated school-based health services have the potential to address the unmet health needs of children experiencing disadvantage, yet these models remain poorly evaluated. The current article examines an integrated social and health care hub located on the grounds of a regional Australian public primary school, the Our Mia Mia Wellbeing Hub, to identify critical success factors for this service and others like it. METHODS: Semi-structured qualitative interviews were conducted with N = 55 multi-sector stakeholders comprising parents, students, school staff, social and health care providers, and local Aboriginal community members. Interview transcripts were analysed according to a grounded theory approach. RESULTS: Six themes emerged from the analysis, reflecting important success factors for the model: service accessibility; service coordination; integration of education and health systems; trust; community partnerships; and perceptions of health. CONCLUSIONS: Findings highlighted Our Mia Mia as a promising model of care, yet also revealed important challenges for the service as it responds to the varied priorities of the stakeholders it serves. SO WHAT?: Through capturing the perspectives of a large number of stakeholders, the current study provides valuable insight into key challenges and success factors for Our Mia Mia; these learnings can guide the development of other emerging school-based health services and integrated care hubs.
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".