I do not expect much ikigai from work: A failed link between employment and well-being among adults with serious mental illness
Bibliographic record
Abstract
BACKGROUND: Researchers argue that employment positively affects the well-being of people with serious mental illness. However, empirical studies have provided limited support for these hypotheses. OBJECTIVE: This study aimed to investigate perceptions of employment in relation to the meaning and purpose of life, an important aspect of well-being, among people with serious mental illness. METHOD: Qualitative research design was employed. Psychiatric service users with a history of employment (n = 21) were recruited from Japan. Photo-elicitation interviews were conducted, and the interview data were analyzed using thematic analysis. RESULTS: Employment was recognized as a source of life meaning and purpose when it reflected personal values such as mastery and contribution to society. Employment was not recognized as relevant to life’s meaning and purpose if it was regarded as an instrumental activity for making a living. Nevertheless, participants generally agreed that employment was indispensable because it was essential for fulfilling their basic needs and overcoming the stigma of mental illness. CONCLUSION: Our results demonstrate diverse attitudes toward employment among people with serious mental illness, which may explain why employment had only a small effect on well-being.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".