Narrative development and supported employment of persons with severe mental illness
Bibliographic record
Abstract
Background: While the relationship between objective recovery and work among persons with severe mental illness (SMI) is well-established, few studies have examined the link between subjective recovery and employment.Aims: The study investigated the prospective relationship between narrative development at the start of supported employment (SE) and positive work outcomes.Methods: The authors employed a time-limited, mixed-method longitudinal design to examine the relationship between the baseline narrative development of 38 SE participants with SMI and employment outcomes eight months later, as well as whether narratives evolved over the course of the study.Results: While narrative development was unrelated to work for the 59% of participants who were employed at the end of the study, unemployed individuals showed more developed baseline narratives overall, as well as enriched baseline emotional connectedness and social worth. Higher emotional connectedness at the start of SE programs was predictive of fewer hours worked eight months later, controlling for executive functioning, negative symptoms and self-esteem. Although workers showed no narrative changes over time, those without work demonstrated increased agency over the eight months of the study.Conclusion: Further research is warranted to clarify the relationship between richer personal narratives and unemployment.
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 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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".