The MRI Self-Efficacy Scale for Children: Development and Preliminary Psychometrics
Bibliographic record
Abstract
OBJECTIVE: Magnetic resonance imaging (MRI) is a common procedure that can be distressing for children. Although not yet studied in the context of pediatric medical procedures, self-efficacy may be a good predictor of procedural stress and a clinically feasible target for behavioral intervention. The objectives of this study were to develop the MRI Self-Efficacy Scale for Children (MRI-SEC) and assess the preliminary psychometric properties. METHODS: Development of the MRI-SEC was informed by literature searches and feedback from healthcare providers. Twenty child-parent dyads naïve to MRI and 10 child-parent dyads with MRI experience completed the MRI-SEC to assess the comprehensibility and ease of use, and to inform item and scale refinement. The final version includes four practice items and 12 items directly assessing MRI self-efficacy. To evaluate the psychometric properties, 127 children (ages 6-12) and parents naïve to MRI completed the MRI-SEC, and a series of measures to assess construct validity. To evaluate test-retest reliability 27 children completed the MRI-SEC a second time. RESULTS: The MRI-SEC demonstrated acceptable internal consistency, test-retest reliability, and convergent validity. CONCLUSION: Development of the MRI-SEC provides an opportunity to better understand the role of self-efficacy as a predictor of procedural stress and cooperation with MRI, informing reliable prediction of children who may benefit from additional support for MRI and the development of tailored behavioral interventions.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".