After the crash: Research-based theater for knowledge transfer
Bibliographic record
Abstract
INTRODUCTION: The aim of this project was to develop and evaluate a research-based dramatic production for the purpose of transferring knowledge about traumatic brain injury (TBI) to health care professionals, managers, and decision makers. METHODS: Using results drawn from six focus group discussions with key stakeholders (consumers, informal caregivers, and health care practitioners experienced in the field of TBI) and relevant scientific literature, a 50-minute play was produced for the purpose of conveying the experiences of TBI survivors, informal care providers, and health practitioners and best practice for TBI care. A self-administered postperformance survey was distributed to audience members at the end of four performances in Ontario, Canada, to evaluate the play's efficacy. Two hundred ninety-one questionnaires were completed. The questionnaire had five questions scored on a 5-item Likert scale with space for open-ended comments. RESULTS: Consistently high mean scores from the questionnaires indicate that theater is a highly efficacious and engaging method of knowledge transfer, particularly for complex material that deals with human emotion and interpersonal relationships. DISCUSSION: Responses supported the effectiveness of drama as a knowledge translation strategy and identified its potential to impact practice positively.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".