Effectiveness of Social Prescribing for Mental Health Across Care Intensity Needs: A Pre–Post Evaluation in Australia
Bibliographic record
Abstract
Social prescribing is an innovative approach that aims to improve health and wellbeing by addressing nonmedical needs through community‐based supports and services. This approach can be delivered through link workers, who work closely with participants to understand their needs and connect them to appropriate services. While social prescribing shows potential for cost savings and improved care quality, further research is needed to understand its effectiveness for people experiencing mental health problems. This pre–post evaluation examined the impact of a social prescribing intervention on health‐related quality of life, subjective health, wellbeing and psychological distress among Australian adults diagnosed with mental health conditions. Adults (aged 18 years and over) from metropolitan Northern Sydney region in New South Wales, who either self‐presented to their general practitioner or self‐referred, were eligible. 398 eligible participants completed the program between December 2021 and August 2024 (data cutoff date) from an ongoing initiative. Participants were assigned to a link worker within 4–6 weeks of initial intake. The 12‐week social prescribing program involved link workers codesigning individualised plans addressing specific needs, providing support and revising plans when required. Validated instruments measuring quality of life, wellbeing and psychological distress were administered preintervention and postintervention, alongside a satisfaction survey. Program enrolment lasted a mean of 17.6 ± 7.6 weeks (median 16.6, range: 1–48). Participants experienced improvements in health‐related quality of life ( p < 0.001), mental wellbeing ( p < 0.001), general wellbeing ( p < 0.001), subjective health ( p < 0.001) and psychological distress ( p < 0.001). Benefits were consistent across binary gender, clustered mental health diagnoses and clustered care intensity levels (lower and higher). These findings suggest that social prescribing program can improve wellbeing outcomes among people experiencing mental illness, highlighting the potential for broader implementation within Australian mental healthcare systems.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".