Lessons learned from the experiences and perspectives of frontline healthcare workers on the COVID-19 response
Bibliographic record
Abstract
Abstract Background During the COVID-19 pandemic, healthcare systems and healthcare workers (HCWs) faced significant demands and unique challenges. In this qualitative study, we explore the effects of the COVID-19 public health policies on British Columbia’s frontline HCWs, describe what worked in the management of the pandemic, and elucidate the lessons learned that could be applied to future pandemic preparedness, recovery and response. Methods This qualitative descriptive study is part of a larger, national multi-case study on pandemic policy communication and uptake. Semi-structured interviews were conducted from November 2020- June 2021 with fourteen HCWs working in long-term care (LTC), acute care and public health settings. Data were inductively coded, and analyzed following a resilience framework for public health emergency preparedness, which emphasizes the essential elements of a public health system, vital to all phases of health emergency management, readiness, response and recovery. Results HCWs experienced confusion, frustration, uncertainty, anxiety, fatigue and stress, during the pandemic and detailed challenges that affected policy implementation. This included communication and coordination inconsistencies between the province and regional health authorities; lack of involvement of frontline staff in pandemic planning; inadequate training and support; inadequate personal protective equipment resource capacity and mobilization; and staffing shortages. HCWs recommended increased collaboration between frontline staff and policy makers, investment in preparing and practicing pandemic plans, and the need for training in emergency management and infection prevention and control. Conclusions Pandemic planning, response and recovery should include inputs from actors/key stakeholders at the provincial, regional and local levels, to facilitate better coordination, communication and outcomes. Also, given the critical roles of frontline HCWs in policy implementation, they should be adequately supported and consideration must be given to how they interpret and act on policies. Bi-directional communication channels should be incorporated between policymakers and frontline HCWs to verify the appropriate adoption of policies, reflective learning, and to ensure policy limitations are being communicated and acted upon by policy makers.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.020 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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".