The work-lives of Canadian Registered Dietitians during the COVID-19 pandemic: a descriptive analysis of survey data
Notice bibliographique
Résumé
Abstract Background Healthcare workers experienced significant disruptions to both their personal and professional lives throughout the COVID-19 pandemic. How healthcare workers were impacted varied, depending on area of specialization, work setting, and factors such as gender. Dietetics is a female-dominated profession and the differential impact on women of the COVID-19 pandemic has been widely reported. While researchers have explored Registered Dietitians’ (RDs) experiences during the pandemic, none have looked explicitly at their experiences of redeployment. The objectives of this study were to better understand: (i) the impact of COVID-19 (and related redeployments) on the work-lives of RDs, (ii) what types of COVID-19 related supports and training were made available to these RDs, and (iii) the impact of RD redeployment on access to RD services. Methods An online survey was administered in June 2022. Any RD that that was publicly-employed in Canada during the pandemic was eligible to participate. The survey included questions related to respondent demographics, professional details, redeployment and training. We conducted descriptive analyses on the quantitative data. Results The survey was completed by 205 eligible RDs. There were notable differences between public health and clinical RDs’ redeployment experiences. Only 17% of clinical RDs had been redeployed, compared to 88% of public health RDs. Public health RDs were redeployed for longer and were more likely to be redeployed to roles that did not required RD-specific knowledge or skills. The most commonly reported mandatory training was for proper use of personal protective equipment. The most commonly reported reasons for a lengthy absence from work were anxiety about contracting COVID-19, school closures and limited child care availability. Conclusions Public health RDs are at the forefront of campaigns to reduce the burden of chronic disease, improve health equity and enhance the sustainability of food systems. Close to 90% of these RDs were redeployed, with many seeing their typical work undone for many months. More research is needed to quantify the consequences of going without a public health nutrition workforce for an extended period of time and to understand the differential impact gender may have had on work experiences during the pandemic.
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 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 | 0,006 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| 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 ».