Vécu des infirmières lors de la pandémie de COVID-19 et conséquences sur leur relation avec les patients : une recherche qualitative consensuelle
Bibliographic record
Abstract
Much the same as other healthcare services, when the COVID-19 pandemic hit, psychiatric hospitals implemented significant and rapid changes in the organization of their services. The aim of this research study is to describe and understand the experience of nurses and nursing supervisors in psychiatric units in the light of the occupational transformations caused by the first wave of the COVID-19 pandemic, as well as the impact of these events on their relationships with patients. A consensual qualitative research study based on Hill's model was implemented. Sixteen individual interviews were conducted with eleven nurses and five nursing supervisors. The themes discussed can be grouped into five areas: aspects of the caregiving relationship, positive aspects of caregivers' experiences, negative aspects of caregivers' experiences, reflections on the post-pandemic era, and the role of supervisors. These five areas can be subdivided into 11 categories and 31 subcategories. Values, attitudes, and behaviors centered around a humanistic caring approach are identified as integral to future development. They appear to be elements of both the transformation process and the desired outcome. In light of these findings, it seems that an immediate rethink of the organization of care is needed.
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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 | 0.011 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".