Exploring How Organizations Can Support Psychological Self-care and Protect its Workers From Moral Distress
Notice bibliographique
Résumé
Context: While health-care workers (HCW) spend their working hours caring for others, many are challenged to find the time and energy for self-care and suffer because of it. Objective: To examine facilitators and barriers facing organizations to a) support health-care workers psychological self-care and b) protect them from moral distress. Study Design and Analysis: Key informants were identified through scholarly and grey literature reviews and snowball recruitment. Potential informants were invited to participate in a one-hour, semi-structured interview. Interviews were audio recorded, transcribed verbatim and analyzed using a thematic approach in NVivo 12. Setting: Interviews were conducted between November 2021 and February 2022 with HCW from multiple disciplines and health-care sectors across Canada. Population Studied: Anyone who self-identified as working in the health-care field, including front line workers and administrators. Instrument: An interview guide was developed for this study informed by the literature review. Outcome Measures: Facilitators and barriers to a) supporting psychological self-care and, b) protection from moral distress at the individual, team, and organizational levels. Results: A total of 29 interviews were completed with 30 participants. Facilitators to supporting psychological self-care included prioritizing self-care and utilization of available resources, positive peer relationships, and supportive leadership, policies, and guidelines. Barriers included hesitancy among HCW to identify themselves as feeling burnt out, an existing unsupportive culture as well as management who were unable to relate to their workers. HCW identified several facilitators to protection from moral distress, including a feeling that their work was making a difference, open communication within teams, and supportive resources such as a wellness team. In contrast, some HCW felt that moral distress was not well understood, and that an unhealthy culture of overwork coupled with a lack of resources were key barriers to protection. Conclusions: In this exploration of psychological health and safety in health-care workplaces across Canada we uncovered a multitude of opportunities for improvement. Participants not only spoke of their challenges to supporting psychological self-care and being able to protect themselves from moral distress but offered many suggestions that organizational leaders can implement in the short-term and longer-term.
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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 | 0,012 | 0,019 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,006 |
| Communication savante | 0,009 | 0,007 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».