An ounce of prevention is worth a pound of cure—the arts as a vehicle for knowledge translation and exchange (KTE) in public health during a pandemic: a realist-informed developmental evaluation research protocol
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 crisis has led to the adoption of strict and coercive preventive measures. The implementation of these measures has generated negative impacts for many communities. The situation is especially worrisome for Indigenous communities in Peru. Therefore, it is necessary to recognise the contribution of the experiential knowledge of Indigenous communities and to implement innovative approaches. The use of art can be a promising avenue for working in partnership with Indigenous communities.The goal of this research is to (1) develop an intervention promoting barrier measures and vaccination to limit the transmission of COVID-19 among Indigenous communities using an arts-based and community-based knowledge translation and exchange (ACKTE) model; and (2) understand the contextual elements and mechanisms associated with the process of developing a preventive intervention using the ACKTE model. METHODOLOGY AND ANALYSIS: This research will take place in Indigenous communities in Peru and will be based on a developmental evaluation guided by the principles of realist evaluation. Members of two Indigenous communities, local authorities, health professionals and artists will participate in the intervention development process as well as in the developmental evaluation. For data collection, we will conduct modified talking circles and semistructured individual interviews with stakeholders as well as an analysis of documents and artistic works produced. ETHICS AND DISSEMINATION OF RESULTS: 's research ethics board. In addition to scientific articles, the results of this research will be disseminated through videos and during an artistic performance.
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 | 0.104 | 0.076 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.052 | 0.010 |
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".