Nurses whistleblowing during the COVID-19 pandemic: Content analysis of the “Je dénonce” platform
Bibliographic record
Abstract
Whistleblowing about critical issues by care staff is an essential component of any well-functioning health care system. During a pandemic, rapid communication of critical information is essential to identify and solve problems. In times of crisis, however, this kind of communication is difficult. In the province of Quebec, Canada, testimonies from nurses, licensed practical nurses (LPNs), and other health professionals during the COVID-19 pandemic indicate that the province’s health care settings have met whistleblowers’ concerns with insufficient corrective measures and, in some cases, retaliation against whistleblowers themselves. This crisis led a union to create an online platform to collect testimonials from the public and quickly make them available to the public and the media. By presenting a content analysis of testimonials submitted by nurses and LPNs, this article aims, on one hand, to identify the issues raised and, on the other, to examine the role and usefulness of this kind of platform for nurses who engage in acts of whistleblowing.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".