The development and usability testing of two digital knowledge translation tools for parents of children with urinary tract infections
Bibliographic record
Abstract
Abstract Urinary tract infections (UTI) are a common source of acute illness for infants and children. Approximately 7-8% of girls and 2% of boys will experience a UTI before they are 8 years old. UTIs may be difficult to identify and treat as symptoms in children are different from expected adult symptoms. A previously conducted systematic review identified four common information needs expressed by parents. More specifically, the research identified that parents had difficulty recognizing signs and symptoms of UTIs, felt disappointed by health care provider’s responses, needed timely and relevant information, and feared the unknown due to lack of UTI knowledge. This demonstrates that more effective knowledge translation tools are needed to satisfy parent information needs. The purpose of this research was to work with parents to develop and test the usability of an interactive infographic and video about UTIs in children. Prototypes were evaluated by parents through usability testing in two Alberta emergency department waiting rooms. Results were positive and overall, the tools were highly rated across all usability items, suggesting that arts-based digital tools are useful mediums for sharing health information with parents.
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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".