Pilot study to test the feasibility and clinical efficacy of a psychosocial care programme for patients with psychosis in low-resource settings
Bibliographic record
Abstract
BACKGROUND: Home-based psychosocial care has the potential to improving outcomes in patients with schizophrenia and related disorders (SCZ). There is lack of India data for such care in early psychosis. We developed the "Saksham" programme, a bespoke self-managed home-based psychosocial care model, available in two formats: manual-based and mobile-application based. With the anticipated success of recruitment of early psychosis cases in our setting, we plan to test the such intervention in this population in future trials. AIM: To assess the feasibility of the Saksham programme intervention in people with SCZ and its clinical efficacy as an adjunct to treatment as usual. METHODS: Seventy-five patient-caregiver pairs (total n=150) were recruited. Patients received either: treatment-as-usual (TAU) (n=25), manual-based Saksham intervention+TAU (n=25), or app-based Saksham intervention+TAU (n=25). Feasibility (i.e. acceptability, practicality, demand, implementation and integration) was assessed at three-months. Participants were assessed for psychopathology, illness-severity, cognition, functioning, disability, and caregiver-coping at baseline, one-month, and three-month. The percentage changes over time were compared across three groups. RESULTS: More found the mobile application-based intervention acceptable and easy-to-use than the manual-based intervention (92 % vs 68 %, and 76 % vs 68 %, respectively). Psychopathology and caregiver-burden improved significantly in all three groups (p<0.05). Cognition, disability, functioning, and caregiver burden improved significantly in the two Saksham intervention groups, with greater improvement in the Saksham app group (p<0.05). CONCLUSION: Home-based intervention is feasible and acceptable in a low-resource setting, with preliminary evidence for effectiveness. These findings need corroboration with randomised controlled trials in early psychosis to ameliorate course of illness.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".