Ethnodrama: An Innovative Knowledge Translation Tool in the Management of Lymphedema
Bibliographic record
Abstract
BACKGROUND: Lymphedema can cause significant physical impairment and quality-of-life issues. Yet there is a gap in knowledge about lymphedema among breast cancer survivors (BCS), and health care professionals (HCP). Ethnodrama is an innovative knowledge translation strategy that uses theatrical performances for dissemination of research results. We evaluated the impact of live ethnodrama on HCP' and BCS' awareness and attitudes in relation to impact of lymphedema on BCS' lives. METHODS: Ethnodrama performances were developed by script writers and a theatre director in collaboration with the investigators and BCS using data from published research and pre-performances workshops. Six interactive live performances were given to audiences of BCS, HCP, and community members in four cities across Canada. After watching these live performances, members of the audiences were asked to complete a paper-based questionnaire regarding their knowledge of lymphedema, and their attitudes and practices toward lymphedema. RESULTS: Of 238 audience members who participated in the survey, 55 (23%) were BCS and 85 (37.5%) were HCP. Most members rated the performances as very effective in changing their (84%) or other people's (93%) understanding of lymphedema; 96% reported being motivated to seek additional information on lymphedema, and 72% of HCP anticipated changes in their practices related to lymphedema screening. Overall no significant differences were noted in responses to ethnodrama between BCS and HCP. Open-ended responses were supportive of the findings from the closed-ended questions. CONCLUSIONS: Our results indicate that ethnodrama performances effectively convey information and positively affecting changes in HCP' and BCS' attitudes toward lymphedema.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".