doi:10.1100/2012/516358 The cientificWorldJOURNAL Research Article Strategies to Fight Stigma toward People withMental Disorders: Perspectives fromDifferent Stakeholders
Bibliographic record
Abstract
Copyright © 2012 Marc Corbière et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. This study aims to provide a more complete and exhaustive perspective on the whole range of potential strategies to fight stigma by considering the perspectives of different stakeholders. Delegates to a Canadian conference were invited to participate in a survey that focused on stigma, from which the responses to the following question were analyzed: tell us briefly what you do to reduce prejudice and stigma toward people with a diagnosis of mental disorder? From 253 participants, 15 categories of strategies to fight stigma were identified from the verbatim (e.g., sharing/encouraging disclosure). These categories fell under six main themes: education, contact, protestation, person centered, working on recovery and social inclusion, and reflexive consciousness. The occurrence of these themes was different among stakeholders (clinical, organizational, and experiential knowledge). For example,
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.828 | 0.542 |
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".