Review Article An Analysis of Canadian Psychiatric Mental Health Nursing through the Junctures of History, Gender, Nursing
Bibliographic record
Abstract
License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. A society that values mental health and helps people live enjoyable andmeaningful lives is a clear aspiration echoed throughout our Canadian health care system. The Mental Health Commission of Canada has put forth a framework for a mental health strategy with goals that reflect the virtue of optimal mental health for all Canadians (Mental Health Commission Canada, 2009). Canadian nurses, the largest group of health care workers, have a vital role in achieving these goals. In Canada, two-thirds of those who experience mental health problems do not receive mental health services (Statistics Canada, 2003). Through a gendered, critical, and sociological perspective the goal of this paper is to further understand how the past has shaped the present state of psychiatric mental health nursing (PMHN).This integrative literature review offers a depiction of Canadian PMHN in light of the intersections of history, gender, education, and quality of nursing work life. Fourteen articles were selected, which provide a partial reflection of contemporary Canadian PMHN. Findings include the association between gender and professional status, inconsistencies in psychiatric nursing education, and the limitations for Canadian nurse practitioners to advance the role of the psychiatric mental health nurse practitioner. 1.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.014 | 0.032 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".