Bibliographic record
Abstract
PURPOSE OF REVIEW: Work is a major determinant of mental health and a socially integrating force. To be excluded from the workforce creates material deprivation, erodes self-confidence, creates a sense of isolation and marginalization and is a key risk factor for mental disability. This review summarizes recent evidence pertaining to employment-related stigma and discrimination experienced by people with mental disabilities. A broad understanding of the stigmatization process is adopted, which includes cognitive, attitudinal, behavioural and structural disadvantages. RECENT FINDINGS: Stigma is both a proximate and a distal cause of employment inequity for people with a mental disability who experience direct discrimination because of prejudicial attitudes from employers and workmates and indirect discrimination owing to historical patterns of disadvantage, structural disincentives against competitive employment and generalized policy neglect. Against this background, modern mental health rehabilitation models and legislative philosophies, which focus on citizenship rights and full social participation, are to be welcomed. Yet, recent findings demonstrate that the legislation remains vulnerable to the very prejudicial attitudes they are intended to abate. SUMMARY: Research conducted during the past year continues to highlight multiple attitudinal and structural barriers that prevent people with mental disabilities from becoming active participants in the competitive labour market.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".