Medical trainee perceived value of community service as they progress through training based on curriculum vitae analysis
Bibliographic record
Abstract
Background: Participation in community service work is often used as a surrogate for measurement of humanitarianism and altruism in medical school applicants during the selection process. Students who continue to be involved in community service during medical school score higher in empathy scales and perform better academically. Despite this, student involvement in community service has not been well studied, particularly during postgraduate training. Methods: First-year medical student (MS1), first year resident (PGY1), and final year resident (FYR) curriculum vitaes (CVs) were collected. CVs were analyzed using NVivo to determine the percentage of each CV committed to demonstrating different activities. These percentages were then analysed for patterns of change as trainees progress through their medical education. Results: Fifty-nine trainees (12 MS1, 24 PGY1, and 23 FYR.) submitted CVs for analysis. Community service gradually becomes a less significant portion of a medical trainee’s CV. Volunteering in the community goes from 22.5% of a medical student applicant´s resume to 2.9% of a graduating resident's CV. Volunteering within the school however remains consistent (11.3–13%). Much of the community volunteer activities are replaced by research, which increases from 19.2%– 43.4% of the CV. Conclusions: Medical trainees place decreasing value on presenting their community service involvement as they progress through training, while research increasingly dominates their CV. However, service activities within their institutions remain constant.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".