Influence of the pandemic on the mental health of professional workers
Notice bibliographique
Résumé
BACKGROUND: This study focuses on the influence of the pandemic on professional workers from an explicitly comparative perspective. High levels of stress and burnout have been reported among professional workers pre-pandemic, but the pandemic has had unique consequences for certain professional workers. Gender has emerged as a particularly important factor. While the existing research yields important insights of mental health concerns among professional workers, there is a need for more research that examines these impacts empirically, explicitly from a comparative perspective across professions taking gender more fully into consideration. METHODS: This paper undertakes a secondary data analysis of two different pan Canadian sources to address the pandemic impact on professional workers: The Canadian Community Health Survey (2020, 2021) administered by Statistics Canada and the Healthy Professional Worker survey (2021). Across the two datasets, we focused on the following professional workers - academics, accountants, dentists, nurses, physicians and teachers - representing a range of work settings and gender composition. Inferential statistics analyses were conducted to provide prevalence rates of self-perceived worsened mental health since the pandemic and to examine the inter-group differences. RESULTS: Statistical analysis of these two data sources revealed a significant effect of the pandemic on the mental health of professional workers, that there were differences across professional workers and that gender had a notable effect both at the individual and professional level. This included significant differences in self-reported mental health, distress, burnout and presenteeism prior to and during the pandemic, as well as the overall impact of the pandemic on mental health. The high levels of distress and burnout during the pandemic were particularly evident in nursing, teaching, and midwifery - professions where women predominate. CONCLUSIONS: Interventions to address the mental health consequences of the pandemic, including their unique gendered and professional dimensions, should consider the intersecting influences and differences revealed through our analysis. In addition to being gender sensitive, interventions need to take into account the unique circumstances of each profession to better respond to the mental health needs of all genders within each professional group.
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 | 0,007 |
| 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 | 0,002 | 0,002 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,002 |
| Intégrité de la recherche | 0,000 | 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 ».