Development and usability testing of two arts-based knowledge translation tools for parents about pediatric fever
Bibliographic record
Abstract
Abstract Fever is defined as an elevated body temperature greater or equal to 38 degrees celsius when measured via the ear canal. It is a common bodily response in children and is typically a benign process that is self-limiting. However, fever can be an anxiety provoking event for some parents because their child can look unwell and become irritable as a result. Past attempts at translating medical knowledge about fever and its management strategies into parent-friendly formats exist; however, parent misperceptions about definition and management persist despite these educational tools. Our research team employs patient engagement techniques to develop resources for parents to enhance the uptake of complex medical knowledge. First, our research group conducts qualitative interviews and knowledge synthesis of the literature. Following analysis, salient themes are used to develop a script and skeleton for our videos and infographics, respectively. Employing this same process, this paper discusses the development and usability testing of two digital tools for fever. Prototypes for the video and infographic were tested by parents in urban and remote emergency department (ED) waiting rooms. A total of 58 surveys were completed by parents. Overall, parents rated both the fever video and infographic favourably, suggesting that patient engaged research methods and digital formats are mediums that can facilitate knowledge transfer.
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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 | 0.014 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".