[From stigma to exclusion of individuals with mental health conditions: Analyzing the perceptions and emotions of stakeholders engaged in the work return and reintegration processes].
Bibliographic record
Abstract
Objectives The stigma surrounding individuals with mental conditions persists, making their return to work or access to employment complex. These negative stereotypes are often internalized by the affected individuals, leading to self-stigmatization. Literature shows that the medical and entrepreneurial sectors harbor many prejudices and apprehensions towards these individuals. In contrast, the fields of rehabilitation and employment support, which frequently interact with these individuals and use recovery approaches, show less resistance. Although the stigma of these populations is relatively well-documented, few studies have differentiated the perceptions of actors responsible for implementing their return and reintegration into work. This study aims to identify and compare these actors' tendencies to stigmatize individuals with mental conditions and their intentions of professional inclusion. Method As part of the international survey by the community of practice in mental health and work (CoP-SMT), data was collected through an online quantitative survey involving 586 actors in the return to work and reintegration processes. Four actor systems were identified: health, corporate, employment support, and individuals with mental disorders on sick leave or job seeking. The questionnaire included a two-factor stigma scale (perceived organizational burden and negative emotions) and a professional inclusion intention measure with three indicators (avoidance, paternalism, and collaboration). Factor analysis and internal consistency validated the psychometric qualities of these measures. Variance analyses with post-hoc comparisons allowed the comparison of responses from categories of 20 or more actors, reducing the sample to 473 participants. Results The results highlight a significant effect of the actor system on stigma and professional inclusion intention. They reveal that actors involved before employment (rehabilitation and employment support) are less prone to stigmatize and more open to including individuals returning or reintegrating into work than those within the company (hierarchy, colleagues, and occupational health professionals). They also show the persistence of self-stigmatization among individuals with mental health issues returning or reintegrating into work. Conclusion To better interpret the results, the experience of coexistence and inclusion in the ordinary work environment of individuals with mental health conditions is explored, considering the roles of various stakeholders. Knowledge, conceptual orientations around mental health, and collaborative multidisciplinary work are suggested as anti-stigma strategies. Based on these findings, research perspectives and recommendations are formulated.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| 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".