Health workforce surge capacity during the COVID‐19 pandemic and other global respiratory disease outbreaks: A systematic review of health system requirements and responses
Bibliographic record
Abstract
Abstract Health system decision‐makers need comprehensive evidence to mitigate surges in the demand for human resources for health (HRH) during infectious disease outbreaks. This study aimed to assess the state of the evidence on policy and planning responses to HRH surge capacity during the coronavirus disease (COVID‐19) pandemic and other viral respiratory disease outbreaks of global significance in the 21st century. We systematically searched eight bibliographic databases to extract primary research articles published between January 2000 and June 2020 capturing temporal changes in health workforce requirements and responses surrounding respiratory virus pandemics. Following the Preferred Reporting Items for Systematic Reviews and Meta‐analyses standard, 16 studies met our inclusion criteria. Five focused on COVID‐19, three on H1N1, and eight modelled a hypothetical pandemic. Investigations of different training, mobilization, and redeployment options to address pandemic‐time health system capacity were reviewed; however, few scenarios drew on observational HRH data, and heterogeneity of study approaches and outcomes generally precluded comparability across contexts. Notable evidence gaps included occupational and psychosocial factors affecting healthcare workers' absenteeism and risk of burnout, gendered considerations of HRH capacity, evaluations in low‐ and lower‐middle income countries, and policy‐actionable assessments to inform post‐pandemic recovery and sustainability of services for noncommunicable disease management.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".