Suicide prevention in a virtual environment: a roadmap for simulation-based education.
Bibliographic record
Abstract
OBJECTIVE: to build and validate a simulation-based education roadmap on suicide prevention in the virtual environment. METHOD: methodological research subdivided into a development and validation stage. The roadmap was built using a previously drafted template based on international guidelines on good clinical simulation practices and scientific literature on suicide prevention in the virtual environment. For validation, the roadmap was validated by experts through self-application of an assessment form with answers based on "adequate, fair, and inadequate", with a field for suggestions. Descriptive statistics and the Content Validity Index (CVI≥0.8) were used. RESULTS: nine experts took part in the study, the majority of whom were nurses (66.7%), female (55.6%), with an average age of 42.22 years. All the items in the roadmap met the acceptance criteria (CVI≥0.8). CONCLUSION: this study provides a useful roadmap for teaching suicide prevention in the virtual environment. BACKGROUND: (1) Innovative study on suicide prevention, simulated teaching, and the virtual environment. (2) Script validated by experts and available in full for simulated teaching. (3) Introduction of a prototype of a fictional virtual social network for simulated practice. (4) Results indicated the appropriateness of the construction, with good agreement in the analyses. (5) The script enhances professional training and development in the mental health context.
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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 | 0.034 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".