Health workforce strategies in response to major health events: a rapid scoping review with lessons learned for the response to the COVID-19 pandemic
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.028 | 0.083 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".