Willingness to Work amongst Australian Frontline Healthcare Workers during Australia’s First Wave of Covid-19 Community Transmission: Results of an Online Survey
Bibliographic record
Abstract
OBJECTIVES: The majority of research investigating healthcare workers' (HCWs) willingness to work during public health emergencies, asks participants to forecast their perceptions based on hypothetical emergencies, rather than in response to the actual public health emergencies they have experienced. This research explored frontline HCWs willingness to work during Australia's first wave of the COVID-19 pandemic among frontline HCWs. METHODS: = 580) completed an online questionnaire regarding their willingness to work during the pandemic. RESULTS: A total of 42% of participants reported being less willing to work during the pandemic compared to before. Availability of personal protective equipment (PPE), concern expressed by family members, and viral exposure were significant barriers. A third of participants disagreed that some level of occupational risk for exposure to infectious disease was acceptable while a quarter of participants had received communications from their workplace concerning obligations to work during COVID-19. CONCLUSIONS: The COVID-19 pandemic has impacted Australian frontline HCWs' willingness to work. Scarcity of PPE and exposure to the virus were the most cited reasons impacting on willingness to work. Appropriate policies and practices should be implemented and communicated efficiently to frontline HCW's. This research provides insight into the lived experiences of Australian healthcare professionals' willingness to work during a pandemic.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".