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Record W4362569011 · doi:10.3917/rsi.151.0043

Vécu des infirmières lors de la pandémie de COVID-19 et conséquences sur leur relation avec les patients : une recherche qualitative consensuelle

2023· article· fr· W4362569011 on OpenAlexaff
Stéphane Moriconi, Manon Lazuckiewiez, Hélène Lefebvre, Dan Lecocq

Bibliographic record

VenueRecherche en soins infirmiers · 2023
Typearticle
Languagefr
FieldPsychology
TopicMental Health Treatment and Access
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsHumanitiesCoronavirus disease 2019 (COVID-19)Political sciencePhilosophyMedicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.026
metaresearch head score (Gemma)0.020
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0260.020
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0010.001

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.

Opus teacher head0.534
GPT teacher head0.582
Teacher spread0.049 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2023
Admission routes1
Has abstractyes

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