Émotions des infirmières au chevet des malades hospitalisés pour la COVID-19. Recherche qualitative consensuelle
Bibliographic record
Abstract
The COVID-19 pandemic has created unprecedented working conditions, with repercussions on the daily lives of nurses. The events experienced positively or negatively in their clinical practice have aroused a variety of emotions for them. The objective of this research is to describe and categorize the events that provoked emotions in nurses who volunteered to accompany COVID-19 victims in a Belgian academic hospital during the first wave of the pandemic by identifying what these emotions were. The researchers used Hill's Consensual Qualitative Research method. Nineteen semi-structured individual interviews were conducted. After the full transcription of the recordings, the data were analyzed by the research team. The results show that the emotions felt by the participants were caused by thirty-seven types of events (categories) grouped into nine families (domains). COVID-19 is viewed negatively by the participants who express fear of this serious and contagious disease. When they talk about the experiences of patients and their families, their discourse alternates between joy at having been able to provide help and care and sadness at not having been able to be effective in all circumstances. Participants share a positive experience and express joy in recalling the COVID-19 outbreak as an exceptional event that they coped with through their personal and professional experience and resources, their relationships with colleagues on the interprofessional team, and the responses of the nursing department and hospital.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".