Causal attributions of job loss among people with psychiatric disabilities.
Bibliographic record
Abstract
OBJECTIVE: Guided by Weiner's attribution theory (1985), the aim of this study is to describe the reasons given by people with psychiatric disabilities to explain job loss. METHODS: Using a sample of 126 people with psychiatric disabilities participating in a prospective study design, the authors evaluated the causal attributions pattern to explain job loss. During a 9-month follow-up phone interview, clients of supported employment programs were asked to explain the reasons why they had lost their jobs. The reasons provided were categorized according to type of job loss (voluntarily vs. involuntarily), locus of control (external vs. internal) and controllability (controllable vs. uncontrollable). RESULTS: The results show that 73% of participants had voluntarily ended their jobs. For the majority of participants, the reasons given to explain job loss were related to external and uncontrollable factors. Moreover, men used more external (34.1% vs. 23%) and uncontrollable (68.2% vs. 40%) reasons than women. Severity of symptoms and level of education also affected the attributional pattern. However, self-esteem, psychiatric diagnosis and work centrality did not correlate significantly to the attributional pattern. CONCLUSION AND IMPLICATIONS FOR PRACTICE: The results demonstrated that reasons given to explain job loss among people with psychiatric disabilities are mostly external. A more systematic evaluation of environmental factors should be put in place to favor longer job tenure for people with psychiatric disabilities.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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".