The development and usability testing of six arts-based knowledge translation tools for parents about COVID-19
Bibliographic record
Abstract
Abstract COVID-19 was declared a global pandemic in March 2020. This novel disease impacted how health information was communicated as information on both the disease itself and on public health guidelines changed rapidly. The purpose of this research was to create knowledge translation (KT) tools about COVID-19 to increase public confidence in science and to encourage vaccine uptake and maintenance of public health measures. Our goal was to develop, evaluate and disseminate innovative KT tools to increase awareness, knowledge and uptake of evidence about COVID-19 among parents and families. The project had two main sources of data collection: 1) qualitative semi-structured interviews with 27 parents whose children had COVID-19 between May 2020 and April 2022 and 2) focus group discussions with 67 parents between October and December 2021 to understand their experiences and information needs related to COVID-19 public health measures, including vaccination, mask wearing, social distancing, and other public health measures. Based on the qualitative findings from semi-structured interviews of parents whose children had COVID-19, we developed two KT tool prototypes: a video and an interactive infographic addressing the management of a child with COVID-19. The qualitative findings from focus groups were used to develop 4 KT tool prototypes on 2 topics: COVID-19 vaccines for children and navigating a child’s social world during the COVID-19 pandemic and beyond. Usability testing of all 6 KT tools was completed by parents from our established networks. The tools were revised based on usability results, and final versions of the tools were made publicly available on our website (echokt.ca) in November 2022. We further disseminated the resources through our social media channels and other established stakeholder networks.
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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 | 0.038 | 0.108 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".