Designing work integration social enterprises that impact the health and wellbeing of people living with serious mental illnesses: an intervention mapping approach
Bibliographic record
Abstract
Work integration social enterprises (WISEs) have been recognised as having the potential to positively impact the health and wellbeing of marginalised populations. The evaluation of the effectiveness of WISE as a health intervention is compromised by both lack of consensus on the critical ingredients of the approach and the inherent need for adaptations to address population-specific concerns within a wide range of local contexts. To help address these issues, and advance the WISE field, we propose the application of intervention mapping, a systematic process framework for the development, implementation, and evaluation of complex public health interventions. Intervention mapping consists of six processes: needs assessment; specifying performance objectives; identifying underlying theory; developing the intervention; implementation and adoption planning; and evaluation planning. To illustrate the intervention mapping processes, we focus its application to WISEs for people with serious mental illnesses. Two brief case studies based on Canadian WISEs illustrate how factors specific to each enterprise influence the implementation of performance objectives to ensure that expected changes occur and, ultimately, influence the health and wellbeing of workers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".