Bibliographic record
Abstract
This paper addresses what is known about workplace stigma and employment inequity for people with mental and emotional problems. For people with serious mental disorders, studies show profound consequences of stigma, including diminished employability, lack of career advancement and poor quality of working life. People with serious mental illnesses are more likely to be unemployed or to be under-employed in inferior positions that are incommensurate with their skills or training. If they return to work following an illness, they often face hostility and reduced responsibilities. The result may be self-stigma and increased disability. Little is yet known about how workplace stigma affects those with less disabling psychological or emotional problems, even though these are likely to be more prevalent in workplace settings. Despite the heavy burden posed by poor mental health in the workplace, there is no regular source of population data relating to workplace stigma, and no evidence base to support the development of best-practice solutions for workplace anti-stigma programs. Suggestions for research are made in light of these gaps.
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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.021 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".