Exploration de l’impopularité des milieux de santé mentale/psychiatrie auprès de la relève infirmière : une revue systématique des écrits
Bibliographic record
Abstract
In every population and country around the world, mental health needs are great and are on the rise. Through their training and their vast field of expertise, nurses are an important lever for addressing the issue of accessibility in these care settings. While the increase in the number of new nursing graduates should have helped this issue, recent data show a sharp increase in the shortage of nurses in these care settings. This systematic review (n=40) using the CINAHL, MEDLINE, PsycArticles, and Scopus databases aims to explore why psychiatric and mental health care settings are unpopular with the next generation of nurses. Guided by Parse's theory, this review identifies three major themes : (1) nursing students' perspectives on mental health issues, (2) the influences of educational interventions on these perspectives, and (3) the factors facilitating and constraining a career in these care settings for new nursing graduates. These results enable a better understanding of what can affect the recruitment of new graduate nurses in mental health/psychiatry, while proposing various levers of intervention to specifically address this issue.
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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 | 0.048 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.023 | 0.027 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.016 | 0.018 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
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".