Rehabilitation Potential Among Institutionalised Homeless Persons with Mental Illness during COVID-19: A Cross-Sectional Study
Bibliographic record
Abstract
Aim: To assess the rehabilitation potential and social well-being among homeless persons with mental illness. Methods: The study utilised quantitative methodology to assess rehabilitation potential and social well-being. It was conducted at Udayam Shelter Home, Kozhikode, Kerala. The district administration initiated the Udayam Project in Kozhikode to rehabilitate destitute individuals rescued from the streets during the Corona Virus Disease 2019 (COVID-19) outbreak in March 2020 and to provide shelter across various camps in the district. The researcher prepared a rehabilitation potential checklist, validated by experts, and a standardised scale was used to assess social well-being. Descriptive statistics were used for quantitative data analysis. Ethical clearance was obtained from the NIMHANS Institute's ethics committee. Results: Over one-third (40%) of the participants were older adults aged above 60 years. All the participants in the study were male. The homeless women identified as needing care and protection were shifted to other centres due to the lack of facilities at the Udayam shelter care home. Previous occupational status revealed that before coming to the rehabilitation centre, most respondents were employed as unskilled labourers. They included daily wagers, lottery sellers, and helpers in hotels and restaurants. Others were skilled labourers, including masons, carpenters, and electricians. Most respondents were from urban and semi-urban areas. Homeless persons with mental illness (HPMI) with mood disorders exhibited better social well-being compared to those with other diagnoses. HPMI diagnosed with psychosis were reported to have very little social actualisation compared to others.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".