[Representations of Mental Disorders and Employment Fit Perceived by Employers of the Regular Labour Market in France].
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,003 | 0,002 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».