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Record W3093695600 · doi:10.1097/hnp.0000000000000418

Empowering Nurses to Provide Humanized Care in Canadian Hospital Care Units

2020· article· en· W3093695600 on OpenAlexaffabout
Laurence Guillaumie, Olivier Boiral, Valérie Desgroseilliers, Nicolas Vonarx, Bernard Roy

Bibliographic record

VenueHolistic Nursing Practice · 2020
Typearticle
Languageen
FieldHealth Professions
TopicEthics in medical practice
Canadian institutionsUniversité du QuébecGovernment of Canada
Fundersnot available
KeywordsNursingStaffingHealth careJob satisfactionMedicinePsychology

Abstract

fetched live from OpenAlex

Previous studies have reported a conflict between nurses' motivation to provide humanized care and practical requirements impeding them from doing so. This exploratory descriptive qualitative study aimed to explore nurses' perspectives on humanized care, the challenges they face, and, most importantly, their recommendations to overcome these barriers. Semistructured individual interviews were conducted with 17 auxiliary and registered nurses working in various health care units in a Canadian hospital. Participants demonstrated a good understanding of what humanized care covers and entails. They also described it as the very core of their profession and main source of job satisfaction. However, nurses reported that they are confronted with organizational barriers, mainly a lack of staff, the burden of administrative tasks, unsuitable physical environments or equipment, and little managerial support. Nurses stressed the need for a cultural change in managerial practices in order to be able to improve their provision of humanized care. Based on the findings, 4 structuring recommendations were identified: adopting an institutional policy promoting the implementation of humanized care, incorporating humanized care in nurses' tasks and procedures, improving participatory management, and ensuring adequate staffing.

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)0.002
metaresearch head score (Gemma)0.176
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.176
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.006
Insufficient payload (model declined to judge)0.0000.001

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 head0.119
GPT teacher head0.525
Teacher spread0.406 · 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; a candidate call from one teacher head, not a consensus.

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

Citations9
Published2020
Admission routes2
Has abstractyes

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