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Record W2527905747 · doi:10.1097/pec.0000000000000936

Web-Based Tools for Educating Caregivers About Childhood Fever

2016· article· en· W2527905747 on OpenAlexaff
Lara Hart, Rashmi Nedadur, Jaime Reardon, Natalie Sirizzotti, Caroline Poonai, Kathy N. Speechley, Jay Loftus, Michael R. Miller, Marina Salvadori, Amanda Spadafora, Naveen Poonai

Bibliographic record

VenuePediatric Emergency Care · 2016
Typearticle
Languageen
FieldMedicine
TopicThermal Regulation in Medicine
Canadian institutionsWestern University
Fundersnot available
KeywordsMedicineWeb applicationMEDLINEWorld Wide Web

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.014
GPT teacher head0.287
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations15
Published2016
Admission routes1
Has abstractyes

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