Blowing the whistle during the first wave of <scp>COVID</scp> ‐19: A case study of Quebec nurses
Bibliographic record
Abstract
The experiences of nurses who blew the whistle during the COVID-19 pandemic have exposed gaps and revealed an urgent need to revisit our understanding of whistleblowing. AIM: The aim was to develop a better understanding of whistleblowing during a pandemic by using the experiences and lessons learned of Quebec nurses who blew the whistle during the first wave of COVID-19 as a case study. More specifically, to explore why and how nurses blew the whistle, what types of wrongdoing triggered their decision to do so and how context shaped the whistleblowing process as well as its consequences (including perceived consequences). DESIGN: The study followed a single-case study design with three embedded units of analysis. METHODS: We used content analysis to analyse 83 news stories and 597 forms posted on a whistleblowing online platform. We also conducted 15 semi-structured interviews with nurses and analysed this data using a thematic analysis approach. Finally, we triangulated the findings. RESULTS: We identified five themes across the case study. (1) During the first wave of COVID-19, Quebec nurses experienced a shifting sense of loyalty and relationship to workplace culture. (2) They witnessed exceedingly high numbers of intersecting wrongdoings amplified by mismanagement and long-standing issues. (3) They reported a lack of trust and transparency; thus, a need for external whistleblowing. (4) They used whistleblowing to reclaim their rights (notably, the right to speak) and build collective solidarity. (5) Finally, they saw whistleblowing as an act of moral courage in the face of a system in crisis. Together, these themes elucidate why and how nurse whistleblowing is different in pandemic times. CONCLUSION: Our findings offer a more nuanced understanding of nurse whistleblowing and address important gaps in knowledge. They also highlight the need to rethink external whistleblowing, develop whistleblowing tools and advocate for whistleblowing protection. IMPACT: In many ways, the COVID-19 pandemic has challenged our foundational understanding of whistleblowing and, as a result, it has limited the usefulness of existing literature on the topic for reasons that will be brought to light in this paper. We believe that studying the uniqueness of whistleblowing during a pandemic can address this gap by describing why and how health care workers blow the whistle during a pandemic and situating this experience within a broader social, political, organizational context.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".