Development and validation of a self-efficacy scale for clinical decision-making in general paediatrics
Bibliographic record
Abstract
BACKGROUND: Success in a task is not only related to skill, but it is also related to 'self-efficacy', or belief in one's capability to perform that task. No tool currently exists to measure self-efficacy in clinical decision-making in general paediatrics. OBJECTIVE: To develop and provide validity evidence for the General Pediatrics-specific Self-Efficacy (GPedsSE) scale. METHODS: The five-item GPedsSE scale, developed using an expert panel, was matched to the New General Self-Efficacy (NGSE) scale's structure for validity analysis purposes. Thirty-six postgraduate year 1 to postgraduate year 5 paediatric residents of the Montreal Children's Hospital (Montreal, Quebec) completed the GPedsSE and NGSE scales, with items interspersed. RESULTS: The mean (± SD) GPedsSE score was 18.6±2.6 of 25. Total GPedsSE and NGSE scores were moderately correlated (r=0.54, P<0.005). On planned comparison, the GPedsSE score increased with training year (F[1.3]=6.62; P<0.001), while the NGSE score did not (F<0.37). Exploratory factor analysis showed two components, each aligning with a scale. CONCLUSION: The GPedsSE scale contextualizes an existing tool to general paediatrics, a novel concurrent validity approach.
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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".