Attitude change during medical school: a cohort study
Bibliographic record
Abstract
BACKGROUND: Attitudes influence behaviour. Developing and maintaining proper attitudes by medical students can impact on the quality of health care delivered to their patients as they assume the role of doctors. There is a paucity of longitudinal research reports on the extent to which students' attitude scores shift as they progress through medical school. OBJECTIVE: This study examined the change in attitude scores of a large student cohort as they progressed through medical school. Whether student gender is related to attitude change was also investigated. METHOD: Medical students from 3 consecutive classes (1999-2001) participated in this study. Students completed 2 instruments that included the Attitudes Toward Social Issues in Medicine and an in-house tool referred to as the Medical Skills Questionnaire. The instruments were administered at 3 milestones during the course of medical school training (entry, end of preclinical training and end of clerkship). RESULTS: Reliability estimates for total (0.82-0.91) and subscale (0.41-0.81) attitudinal scores were in the acceptable range. Multivariate analyses of variance of mean attitudinal scores indicated a persistent decline in several attitude scores as students progressed through the medical educational programme. Females demonstrated higher attitude scores than males. CONCLUSIONS: As students progress through medical school their attitude scores decline. The reasons for the shift in attitude scores are not clear but they may relate to a ceiling of high attitude scores at entry, loss of idealism and the impact of the unintended curriculum. Further study of the impact of medical education on student attitudes is warranted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".