Developing an inventory of ongoing/unpublished arts and narrative-based approaches as knowledge translation strategies in health care
Bibliographic record
Abstract
Background: Systematic review is an essential component of evidence-based health care, yet limited to published academic literature. We aimed to augment an ongoing systematic review on the use of art and narrative as knowledge translation (KT) strategies.Methods: We describe the development of an exploratory process to complement systematic reviews through locating ongoing or unpublished projects. Data were collected through email communication, social media and a secondary analysis of an ongoing database of projects that involve arts in the health care field. Information gathered included: project title, focus, arts or narrative approaches used, anticipated completion date and links to relevant social media, webpages and applicable materials.Results: The inventory captures the ways in which arts and narrative-based KT initiatives are used within a health care context beyond formal research literature.Conclusions: This exploratory process would facilitate updating systematic reviews later while simultaneously capture themes and emerging ideas in unpublished works.
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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 | 0.356 | 0.416 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.026 | 0.022 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.017 | 0.021 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".