Construct and predictive validity of the Strength of Motivation for Medical School-Revised (SMMS-R) questionnaire: a French validation study
Bibliographic record
Abstract
Motivation is a major indicator of students' learning behaviors. Therefore, researchers require consistent and valid instruments to assess students' motivation. Consequently, motivation has been an important topic in medical education research for the last decade. The present study evaluated the construct and predictive validities of the French version of the Strength of Motivation for Medical School-Revised questionnaire (SMMS-R-FR). Our sample comprised 372 students at three French-speaking medical schools, who filled in the SMMS-R-FR and the Revised two-factor Study Process Questionnaire (R2-SPQ). Results confirmed the three-factor structure of the original SMMS-R questionnaire. Reliabilities were good for the Total Strength of Motivation scale, moderate for the Willingness to Sacrifice and Readiness to Start subscales, and poor (but still acceptable) for the Persistence subscale. Both Total Strength of Motivation and Readiness to Start positively predicted a deep learning approach and negatively predicted a surface learning approach, while Willingness to Sacrifice positively predicted a deep learning approach and Persistence negatively predicted a surface learning approach. Our results both support the SMMS-R- FR's suitability as a tool for measuring motivation in medical students, and suggest that it could be used to guide the development of educational interventions to strengthen motivation.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".