One country, two education models: exploring the pedagogical approaches to training undergraduate nurses for mental health care in Canada
Bibliographic record
Abstract
The training and registration of psychiatric/mental health nurses has a contested past in Canada. One of the consequences of the professional jostling between psychiatry and nursing for control over this area is the unusual circumstances of Canada having two education systems for this specialty. To understand why the schism has taken place and the impact it has had on psychiatric/mental health nursing, the authors have undertaken a critical review of the ontological and epistemological assumptions of these two pedagogical approaches. This review reveals that while the approaches share much in common, groups from both the east and the west receive different levels of mental health-related curriculum within their training. While it could be argued that psychiatric/mental health nursing practice is different enough to warrant its own framework for the preparation of specialist practitioners, there is no clear answer as to whether one of the current models should be implemented over the other. In this context, this paper argues that it is important that psychiatric nurses advocate for a future for the speciality in Canada.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.031 | 0.016 |
| Scholarly communication | 0.016 | 0.006 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| 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".