Preventing accidents in children using community‐based learning
Bibliographic record
Abstract
questions were analysed.For each question, we assessed how well the PICO component was defined where applicable, using a grading rubric adapted from a validated assessment tool, the Fresno Test of Competence in EBM (0 = no definition, 1 = limited definition, 2 = partial definition, 3 = complete definition).Evaluation of results and impact A total of 85 questions were recorded from 27 clinical sessions.Of these, 44 questions were on therapy, 22 on prognosis and 19 on diagnosis.Overall, students fared well in defining 'P' (mean 2.3, standard deviation [SD] 1.1) and 'I' (mean 2.1, SD 1.0).They fared moderately in defining 'O' (mean 1.6, SD 1.3) and worst on 'C' (mean 0.6, SD 1.2).They appeared to be more competent in asking questions about therapy and prognosis than about diagnosis.Specifically, 61.1% and 57.9% achieved complete definitions (scoring 3) of interventions and prognostic indicators, respectively, although outcomes were missing in 41.7% of questions on therapy and 21.1% of questions on prognosis.By contrast, when questions on diagnosis were asked, only 36.4% included an index diagnostic test and 19.2% a reference standard.Learning to ask questions at the bedside using the PICO framework stimulates thinking on specific aspects of a patient's problems beyond the generic condition.While formulating clinical questions, students tended to focus on patient characteristics and single interventions, neglecting alternative management strategies and relevant outcomes.Clinical teaching needs to encourage the recognition of variations in diagnostic plans and management strategies tailored to specific outcomes of interest for individual patients.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".