The concept of “benefit finding” for people at different stages of recovery from mental illness; a Japanese study
Bibliographic record
Abstract
BACKGROUND: Benefit finding is defined as finding benefits through the struggle with adversity. AIM: This study explored benefit finding at different stages of recovery among people with severe mental illness in Japan. METHODS: A cross-sectional questionnaire survey, which contained both open-ended questions regarding benefit finding and the Recovery Assessment Scale (RAS), was conducted. Of the responses received from 193 (61%) of 319 individuals with mental illness, responses about benefit finding from 94 questionnaires was analyzed using content analysis (males: 57%; females: 43%; average age: 45 years). Each response about benefit finding was classified into one of three groups according to the stages of recovery by their RAS score (i.e. low, middle or high). RESULTS: The group with higher recovery scores provided more examples of benefit finding, although almost a quarter of examples of benefit finding were provided by the low-RAS group. Different benefit finding characteristics were found between groups of people at different stages of recovery. CONCLUSION: While individuals with higher recovery scores are likely to find a variety of benefits, even individuals with lower recovery scores are capable of benefit finding.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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".