Novel Web-Based Drop-In Mindfulness Sessions (Pause-4-Providers) to Enhance Well-Being Among Health Care Workers During the COVID-19 Pandemic: Descriptive and Qualitative Study
Notice bibliographique
Résumé
BACKGROUND: The COVID-19 pandemic exerted extraordinary pressure on health care workers (HCWs), imperiling their well-being and mental health. In response to the urgent demand to provide barrier-free support for the health care workforce, Pause-4-Providers implemented 30-minute live web-based drop-in mindfulness sessions for HCWs. OBJECTIVE: This study aims to evaluate the use, feasibility, satisfaction, and acceptability of a novel mindfulness program aimed at enhancing the well-being of HCWs during the COVID-19 pandemic. METHODS: Accrual for the study continued throughout the first 3 pandemic waves, and attendees of ≥1 session were invited to participate. The evaluation framework included descriptive characteristics, including participant demographics, resilience at work, and single-item burnout scores; feedback questionnaires on reasons attended, benefits, and satisfaction; qualitative interviews to further understand participant experience, satisfaction, benefits, enablers, and barriers; and the number of participants in each session summarized according to the pandemic wave. RESULTS: We collected descriptive statistics from 50 consenting HCWs. Approximately half of the participants (24/50, 48%) attended >1 session. The study participants were predominantly female individuals (40/50, 80%) and comprised physicians (17/50, 34%), nurses (9/50, 18%), and other HCWs (24/50, 48%), who were largely from Ontario (41/50, 82%). Of 50 attendees, 26 (52%) endorsed feeling burned out. The highest attendance was in May 2020 and January 2021, corresponding to the first and second pandemic waves. The participants endorsed high levels of satisfaction (43/47, 92%). The most cited reasons for attending the program were to relax (38/48, 79%), manage stress or anxiety (36/48, 75%), wish for loving kindness or self-compassion (30/48, 64%), learn mindfulness (30/48, 64%), and seek help with emotional reactivity (25/48, 53%). Qualitative interviews with 15 out of 50 (30%) participants identified positive personal and professional impacts. Personal impacts revealed that participation helped HCWs to relax, manage stress, care for themselves, sleep better, reduce isolation, and feel recognized. Professional impacts included having a toolbox of mindfulness techniques, using mindfulness moments, and being calmer at work. Some participants noted that they shared techniques with their colleagues. The reported barriers included participants' needing time to prioritize themselves, fatigue, forgetting to apply skills on the job, and finding a private place to participate. CONCLUSIONS: The Pause-4-Providers participants reported that the web-based groups were accessible; appreciated the format, content, and faculty; and had high levels of satisfaction with the program. Both novel format (eg, drop-in, live, web-based, anonymous, brief, and shared activity with other HCWs) and content (eg, themed mindfulness practices including micropractices, with workplace applications) were enablers to participation. This study of HCW support sessions was limited by the low number of consenting participants and the rolling enrollment project design; however, the findings suggest that a drop-in web-based mindfulness program has the potential to support the well-being of HCWs.
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 | 0,004 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,001 | 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) | 0,002 | 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 ».