The CREATE strategy of rehabilitation and recovery for mental illness in low resource settings: Development processes and evaluation from a proof of concept study in Kenya
Bibliographic record
Abstract
A significant burden and treatment gap associated with serious mental illness (SMI) exists in low- and middle-income countries (LMIC). In order to address these issues, a range of community-based rehabilitation models in alignment with the Capabilities Approach is necessary, including opportunities for people with SMI to be rapidly engaged in meaningful work within their communities. This paper outlines the development processes and evaluation of the proof of concept for the Community REcovery Achieved Through Entrepreneurship (CREATE) strategy in Kenya. The CREATE strategy pairs Work Integration Social Enterprises (WISE) with a focused, low-cost, recovery-oriented Psychosocial Rehabilitation (PSR) toolkit within a peer involvement/support model as a means of addressing marginalization and fostering health, wellbeing, and participation of people living with SMI. Qualitative research methods were used to understand the experiences of a range of stakeholders involved in CREATE implementation in Kenya. Three overarching themes explicated the experiences of the various stakeholders involved: (1) experiencing a business like other businesses – yet different; (2) expanding capacities, opportunities, and networks through meaningful work; and (3) promoting knowledge and empowerment through education. Within each theme, there was evidence of the attenuation of central elements of marginalization. The CREATE strategy demonstrated acceptability and a variety of potential positive impacts in a low resource setting in Kenya. Application of the CREATE strategy to other low resource settings within Kenya and other LMIC merits further exploration.
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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 | 0.030 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".