Measuring Strength of Motivation for Medical School
Bibliographic record
Abstract
PURPOSE: Students vary in their strength of motivation to start and pursue medical training. This study was conducted to investigate the psychometric properties of a Strength of Motivation for Medical School (SMMS) questionnaire. METHOD: The questionnaire was designed using an iterative method. The instrument was applied to medical students (N= 296) at the start of medical school and to potential applicants (N= 147). The stability of the concept over a six month's time and associations with other motivation measures were studied. A separate group of potential applicants and their parents (N= 169) were asked to validate the items of the questionnaire. RESULTS: Cronbach's alpha reliability of .79 was found. Test-retest reliability of SMMS-scores with a six months interval was .71. Little to no association with specific dimensions of motivation was found, except for a negative correlation with 'ambivalence towards studying'. SMMS-scores were associated with potential applicants' plans to apply for medical school (Spearman's rho .65) and differentially with potential applicants' and their parents' judgements of item validities (.13 to .57). CONCLUSIONS: The SMMS-questionnaire appears to be a reliable and valid instrument to measure strength of motivation for medical training in students who have just entered medical school. It may be used to evaluate the validity of selection procedures and to identify associated variables that could be used in selection procedures.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".