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
Bibliographic record
Abstract
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.
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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 | 0.039 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".