Understanding needs of stakeholders and outcomes desired from a home-based intervention program for “difficult to treat” schizophrenia and related disorders
Bibliographic record
Abstract
Background: We aimed to understand the needs of service users - families and patients with schizophrenia and related disorders, and mental health professionals (MHPs) and expectations from a home-based psychosocial intervention program in Indian setting. Materials and Methods: We conducted four focus group discussions (FGDs) with families, patients with schizophrenia and related disorders and MHPs. Two FGDs were conducted with families and one each with the patients and MHPs. Participants in families and MHP group were asked about their primary concerns in caring for the patients, perceived needs of patients and the areas that can be targeted through a home-based psychosocial intervention program. All FGDs were audio-recorded and verbatim transcribed. Content analysis of the data was done to obtain a final list of needs and expected outcomes from a psychosocial intervention supported by families. Results: Six key priority needs were identified for intervention: medication adherence, activities of daily living, promoting physical health, engagement in meaningful work, building of social and support networks and information about all aspects of illness. Priority outcomes identified by MHPs were mostly clinical like symptom reduction, fewer rehospitalisation while families and patients focused more on psychosocial outcomes, such as improvement of wellbeing, having relationships, engagement in meaningful activities, better organization of the day, increased self-respect, reduced stress, lesser interference, and critical comments. All groups suggested that book or mobile app or video could be used. Conclusion: This qualitative study shows that while both clinicians and service users consider recovery from schizophrenia and related disorders to be important, they differ on what they prioritise.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".