[Representations of Mental Disorders and Employment Fit Perceived by Employers of the Regular Labour Market in France].
Bibliographic record
Abstract
Introduction People with mental disorders face stigma and discriminatory hiring practices in the competitive labour market. This study on employers' representations of mental disorders provides knowledge regarding the specifics of their negative perceptions for this population, which appears to be an important barrier to their inclusion in the workplace. Heilman's lack of fit model (1983) enabled to show that recruiters seek to match the characteristics they perceive in candidates with those they deem necessary to succeed in the organization. A lack of fit between the two components-candidates and the selection criteria-would explain the non-selection of the applicant. This psychological process can be applied to the recruitment of people with psychiatric disabilities.Objectives The goal of this study was to identify employers' representations towards mental disorder in general and in the workplace particularly, as well as to determine the prerequisites for hiring this population. As such, this would allow to better understand the psychological processes involved in the exclusion of people with psychiatric disabilities.Method In a qualitative study, 29 semi-structured interviews were conducted with employers and HR Department representatives of organizations in France that were under the French legal obligation to hire people with a disability (organizations having more than 20 employees). We used the free association technique to identify representational contents concerning mental disorder. Qualitative data on the essential prerequisites for recruitment were collected through open-ended questions. The data were processed by a categorical content analysis conducted independently by three researchers. The structure of the representation was identified by distinguishing the components of the central nucleus from those of the peripheral nucleus according to the two criteria of the method of Moliner (1994): the index of popularity of each element and the co-occurrence between each element of the representation.Results Results revealed negative representations of people with mental disorders, focusing on social deviance and harm to society, believing that people with mental disorders would have non-standard skills and behaviours and would be socially disruptive and burdensome, particularly in the workplace. The analysis of the prerequisites for hiring persons with psychiatric disabilities showed how these representations towards mental disorders are barriers for their recruitment, mainly linked to a perceived lack of employment fit.Conclusion Future avenues of research and actions are suggested. They are as follows: learning, education on mental disorders, training and specific techniques to reduce organizational stakeholders' stereotypes and prejudice. Also, supporting stakeholders for the inclusion of people with mental disorders in the workplace appears fundamental, especially by improving recruitment and integration practises.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".