Do medical students have the knowledge needed to maximize organ donation rates?
Bibliographic record
Abstract
INTRODUCTION: The chronic shortage of organs for donation could be improved by increasing the numbers of potential and actual donors. Physicians can play a key role in solving this problem but may miss opportunities because they lack knowledge about organ donation to answer questions or concerns. Education of physicians early in their careers may lead to better procurement rates for donor organs. We carried out a study at Queen's University in Kingston, Ont., to determine whether medical students have sufficient knowledge of topics shown to affect organ donation rates. METHODS: Medical students from years 1-4 completed a self-administered questionnaire. Section 1 tested general knowledge about organ donation; section 2 tested the students' ability to identify potential donors; and section 3 dealt with the approach to the potential donor's family. Univariate predictors of mean test scores were assessed using the t-test. RESULTS: Of 322 medical students who received the questionnaire, 260 (81%) responded. The mean age of the students was 25 years and 54% were men. The mean knowledge score was 6.7 out of a possible score of 14. Third-year students had the best knowledge scores (7.6), followed by fourth- (7.4), second- (6.6) and first-year students (5.7). Teaching about organ donation and a student's comfort with approaching a family for organ donation were also predictive of higher knowledge scores. There was no correlation between knowledge score and age, gender or whether the student was carrying a signed donor card. Knowledge scores were low in all 3 sections. Thirty-six percent of students did not know that brain death means that the patient is dead rather than in a coma. Half the medical students believed that people of certain religious groups should not be approached about organ donation. CONCLUSIONS: Medical students possess limited knowledge about organ donation topics important for maximizing procurement rates. A teaching intervention designed to target these shortcomings may be beneficial.
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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 | 0.001 | 0.011 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".