Blowing the whistle during the first wave of <scp>COVID</scp> ‐19: A case study of Quebec nurses
Notice bibliographique
Résumé
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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Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,014 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».