Training of children and adolescents’ mental health nursing for nursing students in Japan
Bibliographic record
Abstract
Background: Children and adolescents’ mental health nursing has not been positioned in the curriculum of nursing schools in Japan. The purpose of the present study is to clarify the prevalence of training of children and adolescents’ mental health nursing for nursing students.Methods: A cross-sectional study was conducted from September to October 2013 in Japan. Faculties of pediatric and psychiatric nursing both reported on the educational contents and methods of children and adolescents’ mental health nursing by self-administered questionnaires. To compare prevalence of the training of children and adolescents’ mental health nursing between pediatric and psychiatric nursing, chi-square tests were carried out.Results: The participants in the study were 133 pediatric (39.8%) and 123 psychiatric nursing departments (36.8%). Over 80% of participants had instructed the following 4 educational contents: process of mental development, mental health issues surrounding children and adolescents, related laws and regulations, and classification and treatment for children and adolescents with mental illnesses. Whereas, less than 40% of them had instructed the other 3 contents: nursing care for children and adolescents with mental illnesses, support agency for children and adolescents, and family support. Pediatric nursing had significantly higher prevalence than psychiatric nursing among process of mental development, mental health issues surrounding children and adolescents, and related laws and regulations.Conclusions: Japanese nursing schools have dealt with basic knowledge of mental health with children and adolescents. It will be a challenge in the future to enhance training of practical nursing.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".