Examining the impact of early longitudinal patient exposure on medical students’ career choices
Bibliographic record
Abstract
BACKGROUND: Medical schools include career direction experiences to help students make informed career decisions. Most experiences are short, precluding students from attaining adequate exposure to long-term encounters within medicine. We investigated the impact of the First Patient Program (FPP), which fosters longitudinal patient exposure by pairing junior medical students with chronically ill patients through their healthcare journey, in instilling career direction. METHODS: Medical students who completed at least 6-months in the FPP participated in a cross-sectional survey. Students' answers were analyzed with respect to the number of FPP appointments attended. Thematic analysis was conducted to explore qualitative responses. RESULTS: One hundred and forty-eight students participated in the survey. Only 28 (19%) students stated that the FPP informed their career decisions. Thirty-nine percent of students who attended four or more appointments indicated that the FPP informed their career decisions, compared to 16% of students who attended less (p=0.021). Thematic analysis revealed two themes: 1) Students focused mainly on patient encounters within FPP; and 2) Students sought career directions from other experiences. CONCLUSION: The majority of students did not attain career guidance from the FPP, but rather used the program to understand the impact of chronic illness from the patient's perspective.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".