Systematic review of health workforce surge capacity during COVID-19 and other viral pandemics
Bibliographic record
Abstract
Abstract Background Healthcare decision-makers need comprehensive evidence to mitigate surges in the demand for human resources for health (HRH) during infectious disease outbreaks, in terms of both short- and longer-term impacts. This study aimed to assess the state of the evidence to address HRH surge capacity during COVID-19 and other outbreaks of global significance in the 21st century. Methods We systematically searched eight bibliographic databases to extract primary research articles published between 01/2000-06/2020, capturing temporal changes in HRH requirements and responses surrounding viral respiratory infection pandemics. A systems approach was used, considering providers in hospitals, out-of-hospital systems, emergency medical services, and public health. We narratively synthesized the evidence following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) standard. Results Of the 1,155 retrieved records, 16 studies met our inclusion criteria; of these, 5 focused on COVID-19, 3 on H1N1, and 8 on a hypothetical pandemic. Different training, mobilization, and redeployment options to address pandemic-time health system capacity were assessed. Few governance scenarios drew on observational HRH data allowing for 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. Conclusions This research emphasized the critical need for timely, internationally comparable, and equity-informative HRH data and research to enhance preparedness, response, and recovery policies for this and future pandemics. Full paper is available at: https://doi.org/10.1002/hpm.3137 Key messages The COVID-19 pandemic has highlighted the critical need for enhanced health workforce data and research, including better tracking of demographics, exposures, infections and deaths of health workers. Although women comprise 70% of the health workforce in many countries, gender-blindness persists in the global literature on health workforce research and governance in public health emergencies./bodyt
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.096 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".