Testing the validity of a scenario-based questionnaire to assess the ethical sensitivity of undergraduate medical students
Bibliographic record
Abstract
BACKGROUND: Although medical educators acknowledge the importance of ethics in medical training, there are few validated instruments to assess ethical decision-making. One instrument is the Ethics in Health Care Questionnaire--version 2 (EHCQ-2). The instrument consists of 12 scenarios, each posing an ethical problem in health care, and asking for a decision and rationale. The responses are subjectively scored in four domains: response, issue identification, issue sophistication, and values. GOALS: This study was intended to examine the inter-rater and inter-case reliability of the AHCQ-2 and validity against a national licensing examination of the EHCQ-2 in an international sample. METHODS: A total of 20 final year McMaster students and 45 final year Glasgow students participated in the study. All questionnaires were scored by multiple raters. Generalizability theory was used to examine inter-rater, inter-case and overall test reliability. Validity was assessed by comparing EHCQ-2 scores with scores on the Canadian written licensing examination, both total score and score for the ethics subsection. RESULTS: For both samples, reliability was quite low. Except for the first task, which is multiple choice, inter-rater reliability was 0.08-0.54, and inter-case reliability was 0.14-0.61. Overall test reliability was 0.12-0.54. Correlation between EHCQ-2 task scores and the licensing examination scores ranged from 0.07 to 0.40; there was no evidence that the correlation was higher with the ethics subsection. CONCLUSIONS: The reliability and validity of the measure remains quite low, consistent with other measures of ethical decision-making. However, this does not limit the utility of the instrument as a tool to generate discussion on ethical issues in medicine.
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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 | 0.017 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".