Using theatre as an arts-based knowledge translation strategy for health-related information: a scoping review protocol
Bibliographic record
Abstract
INTRODUCTION: Substantial delays in translating evidence to practice mean that many beneficial and vital advances in medical care are not being used in a timely manner. Traditional knowledge translation (KT) strategies have tended to target academics by disseminating findings in academic journals and at scientific conferences. Alternative strategies, such as theatre-based KT, appear to be effective at targeting broader audiences. The purpose of this scoping review is to collate and understand the current state of science on the use of theatre as a KT strategy. This will allow us to identify gaps in literature, determine the need for a systematic review and develop additional research questions to advance the field. METHODS AND ANALYSIS: . The search strategy, guided by an experienced librarian, will be conducted in PubMed, CINHAL and OVID. Study selection will consist of three stages: (1) initial title and abstract scan by one author to remove irrelevant articles and create a shortlist for double screening, (2) title and abstract scan by two authors, and (3) full-text review by two authors. Included studies will report specifically on the use of theatre as means of KT of health-related information to any target population. Two reviewers will independently extract and chart the data using a standardised data extraction form. Descriptive statistics will be used to produce numerical summaries related to study characteristics, KT strategy characteristics and evaluation characteristics. For those studies that included an evaluation of the theatre production as a KT strategy, we will synthesise the data according to outcome. ETHICS AND DISSEMINATION: Ethical approval was not required for this study. Results will be published in relevant journals, presented at conferences and distributed via social media.
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 | 0.170 | 0.161 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.011 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.007 | 0.008 |
| Research integrity | 0.012 | 0.009 |
| Insufficient payload (model declined to judge) | 0.090 | 0.028 |
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".