Students’ perception on medical professionalism: the psychometric perspective
Bibliographic record
Abstract
BACKGROUND: The main purpose of this study was to identify and understand the structure of latent traits underlying the concept of medical professionalism of Taiwanese students. METHODS: A 32 item questionnaire assessing medical professionalism derived from the definition by the American Board Internal Medicine (ABIM) was distributed to 133 year seven medical students. A five-point rating scale of importance was used to identify the extent of their values or beliefs in each item. RESULTS: The three items perceived most important were: accountability to patients, respect for patients and their families; and integrity and prudence. The least important component underlying professionalism was 'enduring unavoidable risks to oneself when a patient's welfare is at stake'. Factor analysis resulted in eight factors: 'commitment to care' (factor 1); 'righteous and rule-abiding' (factor 2); 'pursuing quality patient care' (factor 3), 'habit of professional practice' (factor 4); 'interpersonal relationship' (factor 5); 'patient-oriented' issues (factor 6); physician's 'self-development' (factor 7); and finally, 'respect for others' (factor 8). Most of the variance was contributed by factor 1 (34.9%). The mean score of factors ranged from 3.84 (factor 1: commitment to care) to 4.7 (factor 8: respect of others), and the reliability alphas ranged from 0.86 to 0.66. CONCLUSIONS: These results of young physicians' professional values have implications for medical school curriculum for improvement.
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 | 0.002 | 0.010 |
| 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 | 0.001 |
| 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".