Work-life boundary management of peer support workers when engaging in virtual mental health support during the COVID-19 pandemic: a qualitative case study
Notice bibliographique
Résumé
BACKGROUND: Mental health care needs have increased since the COVID-19 pandemic was declared. Peer support workers (PSWs) and the organizations that employ them have strived to provide services to meet increasing needs. During pandemic lockdowns in Ontario, Canada, these services moved online and were provided by PSWs from their homes. There is paucity of research that examines how providing mental health support by employees working from home influences their work-life boundaries. This research closes the gap by examining experiences of work-life boundary challenges and boundary management strategies of PSWs. METHODS: A qualitative case study approach was adopted. Interviews with PSWs who held formal, paid positions in a peer support organization were conducted. Data was analyzed thematically using both inductive and deductive approaches. Descriptive coding that closely utilized participants' words was followed by inferential coding that grouped related themes into conceptual categories informed by boundary theory. Member checking was conducted. RESULTS: PSWs provided accounts of work-life boundary challenges that we grouped into three categories: temporal (work schedule encroachments, continuous online presence), physical (minimal workspace segregation, co-presence of household members and pets) and task-related (intersecting work-home activities). Strategies used by PSWs to manage the boundaries consisted of segmenting the work-life domains by creating separate timescapes, spaces and tasks; and integrating domains by allowing some permeability between the areas of work and life. CONCLUSION: The findings from this study can help inform management, practices, future research and policy on health care workforce. The study highlights the need to attend to the consequences of greater work-life integration for mental health workers since their successful practice is largely dependent on maintaining self-care. Training regarding work-life boundary management is highlighted as one of the ways to approach situations where work from home is required.
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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 | 0,041 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 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.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».