Health workforce strategies in response to major health events: a rapid scoping review with lessons learned for the response to the COVID-19 pandemic
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Notice bibliographique
Résumé
BACKGROUND: The early weeks of the COVID-19 pandemic brought multiple concurrent threats-high patient volume and acuity and, simultaneously, increased risk to health workers. Healthcare managers and decision-makers needed to identify strategies to mitigate these adverse conditions. This paper reports on the health workforce strategies implemented in relation to past large-scale emergencies (including natural disasters, extreme weather events, and infectious disease outbreaks). METHODS: We conducted a rapid scoping review of health workforce responses to natural disasters, extreme weather events, and infectious disease outbreaks reported in the literature between January 2000 and April 2020. The 3582 individual results were screened to include articles which described surge responses to past emergencies for which an evaluative component was included in the report. A total of 37 articles were included in our analysis. RESULTS: The reviewed literature describes challenges related to increased demand for health services and a simultaneous decrease in the availability of the workforce. Many articles also described impacts on infrastructure that hindered emergency response. These challenges aligned well with those faced during the early days of the COVID-19 pandemic. In the published literature, the workforce strategies that were described aimed either to increase the numbers of health workers in a given area, to increase the flexibility of the health workforce to meet needs in new ways, or to support and sustain health workers in practice. Workforce responses addressed all types and cadres of health workers and were executed in a wide range of settings. We additionally report on the barriers and facilitators of workforce strategies reported in the literature reviewed. The strategies that were reported in the literature aligned closely with our COVID-specific conceptual framework of workforce capacity levers, suggesting that our framework may have heuristic value across many types of health disasters. CONCLUSIONS: This research highlights a key deficiency with the existing literature on workforce responses to emergencies: most papers lack substantive evaluation of the strategies implemented. Future research on health workforce capacity interventions should include robust evaluation of impact and effectiveness.
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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 | 0,032 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,005 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| 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 découle