Mental health care nursing standards: international perspectives Sarah Benbow, Wafa’a Ta’an, Malene Terp, Marc Haspeslagh and
Bibliographic record
Abstract
Introduction The purpose of this chapter is to describe similarities and differences in psychiatric mental health (PMH) nursing care and standards across diverse global contexts. This chapter is based on the experience of four authors residing in four different countries around the globe. Their experiences are bound to the mental health system of their countries and their own careers and practice environments. These examples reflect the authors’ lens about psychiatric nursing care practice standards and the functioning of nurses within these countries. Specifically, sets of psychiatric nursing standards will be examined from differing cultural contexts: a North American country (Canada), a Middle Eastern country (Jordan), and two European countries (Belgium and Denmark). Definitions of the standards of mental health care will be provided and a description of PMH nursing within a contextual overview of mental health cares in each country (see Table 8.1). Current mental health issues and tensions that impact nursing and PMH nursing standards of practice will be explored. Recommendations for improving mental health care by increasing the role and visibility of psychiatric nurses will be addressed. The chapter will conclude by discussing the similarities and differences related to PMH nursing practice across these diverse contexts.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".