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Record W2793500560 · doi:10.3917/rsi.131.0029

Comment l’employeur peut-il soutenir la formation continue et le développement professionnel infirmier ? Résultats d’une étude qualitative canadienne auprès d’infirmières de soutien à domicile

2018· article· fr· W2793500560 on OpenAlexaffabout
Jérôme Ouellet, Joséphine Mukamurera

Bibliographic record

VenueRecherche en soins infirmiers · 2018
Typearticle
Languagefr
FieldNursing
TopicNursing education and management
Canadian institutionsUniversité de SherbrookeUniversité de Montréal
Fundersnot available
KeywordsHumanitiesPolitical sciencePhilosophy

Abstract

fetched live from OpenAlex

INTRODUCTION: this article provides a new knowledge on employer's support for home care nurses' continuing education. CONTEXT: so far, literature has sustained that providing support to nurses in continuing education is mainly a matter of money. However, only few researchers have been interested in home care, especially in continuing education. OBJECTIVE: one of the objectives of the survey was to identify factors that could influence nurses' commitment, participation and choice in the matter of continuing education activities. METHODS: a qualitative survey was conducted with eight nurses, coming from one clinical home care setting (Québec, Canada), who participated in a semi-structured individual interview. Thematic analysis was used. Results were validated by intra- and inter-rater controls. Furthermore, participants were involved in the process of validation. Results have shown that support given by the employer in the matter of continuing education can be seen into five different aspects : financial, training, affective, instrumental and normative. Despite a lack of financial and training support, most of home care nurses have a positive perception of their employer's support. DISCUSSION: nevertheless, employers should pay more attention to nurses' needs. To do so, nurses should be involved into the process of continuing education at their workplace.

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)0.011
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.572
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.290
GPT teacher head0.452
Teacher spread0.162 · 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 designNot applicable
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
Published2018
Admission routes2
Has abstractyes

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