Analyse phénoménologique interprétative du vécu expérientiel des étudiantes au baccalauréat en sciences infirmières lors d’un stage en santé mentale. Comprendre pour mieux former
Bibliographic record
Abstract
In a global context where populations' mental health needs are growing rapidly, recruiting the next generation of nurses to work in these care settings is particularly problematic. Because of their negative views on mental health issues, nursing students reject such a career path. According to the literature, training programs, particularly clinical immersions, are the main way of mitigating the unpopularity of mental health care among this new generation of nurses. Through an interpretive phenomenological analysis of semi-structured interviews conducted with eleven undergraduate nursing students, this research studied their learning experience during a clinical immersion in mental health care. Anchored in Parse's humanbecoming theory, this study explores the meaning that students attribute to such an experience, the experiential negotiation processes of the practicum setting, and the participants' ability to project themselves beyond the learning experience itself. These results raise various issues related to mental health nursing education, such as the importance of having a nursing role model, as well as various influencing factors related to the rejection of a career in mental health care by the next generation, such as the perception that working in these care settings involves an increased risk of aggression.
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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 | 0.014 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| 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".