Effectiveness of networks of mental health services on continuity of care and social integration
Bibliographic record
Abstract
The shift towards community mental health care has raised concerns about continuity of care for patients with severe mental illness: these patients living in the community are likely to experience re-hospitalization, compulsory commitment, discontinuity of treatment, poor social integration and other adverse events. Network of mental health services has been advocated to provide these patients with coordinated care and to tackle fragmentation across social care, general health care and mental health care. Yet, the evidence these networks are effective is quite limited and inconclusive. In 2010, Belgium implemented a nation-wide reform commissioning networks of social, health, and mental health services. Using a case-control design, we assessed the effectiveness of this reform on continuity of care, social integration, quality of life and hospitalization. Nineteen experimental networks commissioned in 2012 and 2014 and four control areas, including 735 services and 2200 patients were recruited. Patients and their carer were requested to complete a questionnaire. We computed an index of exposition to the reform and outcomes of continuity of care (Alberta Continuity of Service Scale), social integration (SIX score), and quality-of-life (MANSA). Multilevel logistic and multinomial regression were used to assess the effect of the reform controlling for patient socio-demographics and clinical status. Results indicated that higher exposure to the reform was associated with a slight increase in continuity of care but not in social integration neither in quality-of-life or hospitalization. We concluded that networks can achieve only a narrow and limited range of objectives. Key messages: Networks of mental health services can achieve only a narrow and limited range of objectives Networks of mental health services were associated with a slight increase in continuity of care
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.005 | 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".