The web of silence: a qualitative case study of early intervention and support for healthcare workers with mental ill-health
Notice bibliographique
Résumé
BACKGROUND: There is a high rate of stress and mental illness among healthcare workers, yet many continue to work despite symptoms that affect their performance. Workers with mental health issues are typically ostracized and do not get the support that they need. If issues are not addressed, however, they could become worse and compromise the health and safety, not only of the worker, but his/her colleagues and patients. Early identification and support can improve work outcomes and facilitate recovery, but more information is needed about how to facilitate this process in the context of healthcare work. The purpose of this study was to explore the key individual and organizational forces that shape early intervention and support for healthcare workers who are struggling with mental health issues, and to identify barriers and opportunities for change. METHODS: A qualitative, case study in a large, urban healthcare organization was conducted in order to explore the perceptions and experiences of employees across the organization. In-depth interviews were conducted with eight healthcare workers who had experienced mental health issues at work as well as eight workplace stakeholders who interacted with workers who were struggling (managers, coworkers, union leaders). An online survey was completed by an additional 67 employees. Analysis of the interviews and surveys was guided by a process of interpretive description to identify key barriers to early intervention and support. RESULTS: There were many reports of silence and inaction in response to employee mental health issues. Uncertainty in identifying mental health problems, stigma regarding mental ill health, a discourse of professional competence, social tensions, workload pressures, confidentiality expectations and lack of timely access to mental health supports were key forces in preventing employees from getting the help that they needed. Although there were a few exceptions, the overall study findings point to many barriers to supporting employees with mental health issues. CONCLUSIONS: In order to address the complex knowledge, attitudinal, interpersonal and organizational barriers to action, a multi-layered knowledge translation strategy is needed, that considers not only mental health literacy and anti-stigma interventions, but addresses the unique context of the work environment that can act as a barrier to change.
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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, pas un consensus.
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