Organizational responses to the COVID-19 pandemic in Victoria, Australia: A qualitative study across four healthcare settings
Bibliographic record
Abstract
Objective: Organizational responses that support healthcare workers (HCWs) and mitigate health risks are necessary to offset the impact of the COVID-19 pandemic. We aimed to understand how HCWs and key personnel working in healthcare settings in Melbourne, Australia perceived their employing organizations' responses to the COVID-19 pandemic. Method: In this qualitative study, conducted May-July 2021 as part of the longitudinal Coronavirus in Victorian Healthcare and Aged Care Workers (COVIC-HA) study, we purposively sampled and interviewed HCWs and key personnel from healthcare organizations across hospital, ambulance, aged care and primary care (general practice) settings. We also examined HCWs' free-text responses to a question about organizational resources and/or supports from the COVIC-HA Study's baseline survey. We thematically analyzed data using an iterative process. Results: . HCWs valued organizational efforts to engage openly and honesty with staff, and proactive responses such as strategies to enhance workplace safety (e.g., personal protective equipment spotters). Suggestions for improvement identified in the themes included streamlined information processes, greater involvement of HCWs in decision-making, increased investment in staff wellbeing initiatives and sustainable approaches to strengthen the healthcare workforce. Conclusions: This study provides in-depth insights into the challenges and successes of organizational responses across four healthcare settings in the uncertain environment of a pandemic. Future efforts to mitigate the impact of acute stressors on HCWs should include a strong focus on bidirectional communication, effective and realistic strategies to strengthen and sustain the healthcare workforce, and greater investment in flexible and meaningful psychological support and wellbeing initiatives for HCWs.
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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 | 0.012 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| 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".