Understanding the mental health and intention to leave of the public health workforce in Canada during the COVID-19 pandemic: A cross-sectional study
Notice bibliographique
Résumé
BACKGROUND: There is limited evidence about the mental health and intention to leave of the public health workforce in Canada during the COVID-19 pandemic. The objectives of this study were to determine the prevalence of burnout, symptoms of anxiety and depression, and intention to leave among the Canadian public health workforce, and associations with individual and workplace factors. METHODS: A cross-sectional study was conducted using data collected by a Canada-wide survey from November 2022 to January 2023, where participants reported sociodemographic and workplace factors. Mental health outcomes were measured using validated tools including the Oldenburg Burnout Inventory, the 7-item Generalized Anxiety Disorder scale, and the 2-item Patient Health Questionnaire to measure symptoms of depression. Participants were asked to report if they intended to leave their position in public health. Logistic regression was used to estimate adjusted odds ratios (aOR) and 95% confidence intervals (95% CI) for the associations between explanatory variables such as sociodemographic, workplace factors, and outcomes of mental health, and intention to leave public health. RESULTS: Among the 671 participants, the prevalence of burnout, and symptoms of depression and anxiety in the two weeks prior were 64%, 26%, and 22% respectively. 33% of participants reported they were intending to leave their public health position in the coming year. Across all outcomes, sociodemographic factors were largely not associated with mental health and intention to leave. However, an exception to this was that those with 16-20 years of work experience had higher odds of burnout (aOR = 2.16; 95% CI = 1.12-4.18) compared to those with ≤ 5 years of work experience. Many workplace factors were associated with mental health outcomes and intention to leave public health. Those who felt bullied, threatened, or harassed because of work had increased odds of depressive symptoms (aOR = 1.85; 95% CI = 1.28-2.68), burnout (aOR = 1.61; 95% CI = 1.16-2.23), and intention to leave (aOR = 1.64; 95% CI = 1.13-2.37). CONCLUSIONS: During the COVID-19 pandemic, some of the public health workforce experienced negative impacts on their mental health. 33% of the sample indicated an intention to leave their role, which has the potential to exacerbate pre-existing challenges in workforce retention. Study findings create an impetus for policy and practice changes to mitigate risks to mental health and attrition to create safe and healthy working environments for public health workers during public health crises.
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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 | 0,002 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,002 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».