Web-Based Tools for Educating Caregivers About Childhood Fever
Bibliographic record
Abstract
OBJECTIVES: Fever is a common reason for an emergency department visit and misconceptions abound. We assessed the effectiveness of an interactive Web-based module (WBM), read-only Web site (ROW), and written and verbal information (standard of care [SOC]) to educate caregivers about fever in their children. METHODS: Caregivers in the emergency department were randomized to a WBM, ROW, or SOC. Primary outcome was the gain score on a novel questionnaire testing knowledge surrounding measurement and management of fever. Secondary outcome was caregiver satisfaction with the interventions. RESULTS: There were 77, 79, and 77 participants in the WBM, ROW, and SOC groups, respectively. With a maximum of 33 points, Web-based interventions were associated with a significant mean (SD) pretest to immediate posttest gain score of 3.5 (4.2) for WBM (P < 0.001) and 3.5 (4.1) for ROW (P < 0.001) in contrast to a nonsignificant gain score of 0.1 (2.7) for SOC. Mean (SD) caregiver satisfaction scores (out of 32) for the WBM, ROW, and SOC groups were 22.6 (3.2), 20.7 (4.3), and 17 (6.2), respectively. All groups were significantly different from one another in the following rank: WBM > ROW > SOC (P < 0.001). CONCLUSIONS: Web-based interventions are associated with significant improvements in caregiver knowledge about fever and high caregiver satisfaction. These interventions should be used to educate caregivers pending the demonstration of improved patient-centered outcomes.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".