Code White: A branching spherical video learning experience for continuing competency mental health nursing education
Bibliographic record
Abstract
Background Patient aggression toward mental health nurses is a critical issue, impacting clinician anxiety and patient care quality. Traditional training may not adequately prepare nurses for managing aggressive patients in clinical environments, or Code Whites. Branching spherical video learning offers an immersive training approach, allowing nurses to refine their skills in a safe and controlled environment. Methods A mixed-methods design included a quasi-experimental pre-/post-test study and qualitative interviews. Participants were mental health nurses from a Western Canadian acute care mental health and addictions department. Anxiety and confidence were measured using the NASCCDM© and NCSE© scales before and after five training sessions. Qualitative data were gathered through individual interviews and analyzed using NVivo. SPSS was used for quantitative analysis. Results Twenty-two nurses participated, with significant improvements in confidence (12.3%) and self-efficacy (8.3%), alongside an 8.6% reduction in anxiety related to Code White clinical situations. Qualitative themes highlighted benefits such as realism of the scenario, skill reinforcement, and communication enhancement. Conclusion Branching spherical video learning effectively reduced anxiety and enhanced clinician confidence in high-risk psychiatric nursing scenarios. Addressing implementation challenges can maximize its educational potential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".