How are countries supporting health workers? Data from the COVID-19 Health System Response Monitor
Bibliographic record
Abstract
Abstract Background Health workers have been at the forefront of treating and caring for patients with COVID-19. They were often under immense pressure to care for severely ill patients with a new disease, under strict hygiene conditions and with lockdown measures creating practical barriers to working. This study aims to explore the range of mental health, financial and other practical support measures that 36 countries in Europe and Canada have put in place to support health workers and enable them to do their job. Methods We use data extracted from the COVID-19 Health Systems Response Monitor (HSRM). We only consider initiatives implemented outside of clinical settings where COVID-19 patients are treated, and therefore exclude workplace provisions such as availability of personal protective equipment, working time limits or mandatory rest periods. Results We show that countries have implemented a range of measures, ranging from mental health and well-being support initiatives, to providing bonuses and temporary salary increases. Practical measures such as childcare provision and free transport and accommodation have also been implemented to ensure health workers can get to their workplace and have their children looked after. Other initiatives such as offering continuing professional development credits for knowledge learnt during the crisis were also offered in some countries, albeit less frequently. Conclusions While a large number of initiatives have been introduced, often as ad-hoc measures, their effectiveness in helping staff is unknown in most countries. The effectiveness of these initiatives should be evaluated to inform future crisis responses and strategies for health workforce development.
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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 | 0.075 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".