Using theater as an innovative knowledge translation approach for health research: a scoping review protocol
Bibliographic record
Abstract
OBJECTIVE: The objective of this review is to synthesize the existing literature on how theater has been used as a knowledge translation approach for health research and to identify the outcome measures employed for evaluation as well as the facilitators/challenges related to this approach. INTRODUCTION: The use of arts-based knowledge translation methods is relatively new in health research but has already shown to have positive impacts on knowledge, attitudes, policy, and practice. Specifically, theater has proven to be an effective approach for communicating research findings in a way that stimulates thought and discussion on important health-related topics. INCLUSION CRITERIA: This review will include scholarly literature on how theater is being used as a knowledge translation approach for health research. The review will not impose any limitations related to demographic variables, health issues, or settings. The review will consider papers using any study design, and will also consider other literature, such as protocols, descriptive papers, unpublished papers, and evaluation reports. METHODS: This review will be conducted in accordance with the JBI methodology for scoping reviews. The databases to be searched will include CINAHL (EBSCOhost), Embase, MEDLINE (Ovid), Academic Search Premier (EBSCOhost), and Scopus. Google/Google Scholar and ProQuest Dissertations and Theses will also be searched for unpublished studies and gray literature. All literature identified in the search will be screened by 2 independent reviewers and the results will be presented in a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) flow diagram. The data extracted from the included literature will be presented in both tabular and narrative format. REVIEW REGISTRATION: Open Science Framework https://osf.io/gbcpj.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
| gpt | no category Domain: not available · Genre: Protocol About the Canadian research system: no · About a Canadian topic: no | Systematic review | high |
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 | 0.189 | 0.147 |
| Meta-epidemiology (narrow) | 0.005 | 0.006 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.022 | 0.017 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.069 | 0.024 |
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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".