Educating undergraduate general nursing students to conduct Mental State Assessments using high fidelity video simulations that develops learning in the affective domain
Bibliographic record
Abstract
Nursing students require a range of clinical skills to contribute as part of the mental health care team, we would argue that one of the quintessential skills is the ability to complete and accurately record a Mental State Assessment (MSA). Teaching students how to complete a MSA using high fidelity video simulations (HVFS) prepares them for the reality of clinical practice including cognitive skill development and emotional readiness for clinical practicum in acute mental health care settings. In this study, three HVF simulations were created to facilitate students learning, based on well documented evidence supporting the use of video content as a learning media. However our focus is not only on cognitive skill development in conducting mental state assessments, but in distinguishing the approach to learning in the affective domain. Using this method, students learn to confront and manage their own feelings, beliefs and attitudes and in turn regulate their emotional responses in clinical situations. Pre and post workshop evaluations completed by students for the past three years has resulted in consistently high levels of confidence in ability to conduct a MSA after the workshop. The paired t test was used to calculate the difference between pre and post workshop confidence scores, revealing the two-tailed p value of less than .0001 which is considered to be extremely statistically significant. Therefore, it can be concluded that students completing the 3-day workshop had a significant increase in confidence and competence in performing MSA’s.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".