Dramatic insights: A report of the effects of a dramatic production on the learning of student nurses during their mental health course component
Bibliographic record
Abstract
Drama, like nursing, concerns itself with the human condition and finds its resonance in an empathetic response to the person-to-person connection. As part of their clinical experience in the mental health component of their undergraduate nursing course, 80 student nurses attended the play Bearing Witness written by the authors and produced as a joint Faculty production between the Faculty of Nursing and the Department of Drama at the University of Alberta, Edmonton, Canada. The play charts and interweaves the paths to healing of two people following incestuous abuse for one, and political torture for the other, and poses questions to the audience concerning the intent and effects of abuse, and the place and value of forgiveness and forgiving. Some 4-5 months afterwards the students were invited to participate in hour-long, semi-structured interviews to investigate the persistent impressions of the play, the students' perceptions of the themes and relevance and the effect of the play on their learning and development as nurses. The seven students who took part in the interviews expressed that their feelings of empathy and identification with the situation of the characters in the play had a greater impact on their learning than other methods of instruction they had evaluated, particularly in terms of the persistence and processing of the subjective experience. Other notable themes were that of forgiveness, its value in healing and the implications for nursing, and an appreciation of the 'spiritual component' of the total learning experience.
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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 | 0.004 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".