Evaluating a knowledge translation tool for parents about pediatric acute gastroenteritis: a pilot randomized trial
Bibliographic record
Abstract
BACKGROUND: Pediatric acute gastroenteritis (AGE) is a common childhood illness with substantial health, family, and system impacts. Connecting parents to evidence-based patient education is key to effective decision-making and therapeutic management of AGE. Digital knowledge translation (KT) tools offer a promising approach to communicate complex health information to parents; therefore, we developed a whiteboard animation video for parents about AGE. To optimize future effectiveness evaluation of this video, the purpose of this pilot study is to assess feasibility of effectiveness outcomes and specific trial methods in four key trial domains. METHODS: A single-site, parallel-arm, pilot randomized trial will be conducted. The trial will employ quantitative and qualitative methods to evaluate feasibility objectives in key scientific, process, management, and resource domains. Parents seeking care for a child with AGE in the emergency department (ED) over a 6-month period will be randomized to receive the whiteboard animation video or a sham control video. Quantitative data will be collected electronically in the ED and at home (4-10 days post-ED visit). Qualitative data will be collected via semi-structured interviews with experimental condition participants after quantitative data collection. Data will be collected to perform a sample size calculation for a full-scale trial. Scientific outcomes will include parental knowledge, decision regret, and health utilization, and estimation for these outcomes will use confidence intervals (CI) of different widths to illustrate strength of preliminary evidence. CIs will be presented alongside minimum clinically important differences (MCIDs) calculated using two methods: (1) data driven and (2) patient perspective. Descriptive statistics will be calculated to describe process, management, and resource domain outcomes. Qualitative thematic analysis will be conducted to describe additional process, management, and resource outcomes in the experimental group. Analyses will be performed using intention-to-treat. DISCUSSION: This pilot randomized trial will inform the design and conduct of a full-scale, effectiveness trial by gathering key data in four domains: scientific, process, management, and resource. These results will impact the emerging field of KT efforts targeting health consumers and advance the science on the best mode of patient education for acute childhood illnesses. TRIAL REGISTRATION: clinicaltrails.gov registration number NCT03234777. Registered 31 July 2017.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".