International health experiences in postgraduate medical education: A meta-analysis of their effect on graduates’ clinical practice among underserved populations
Bibliographic record
Abstract
BACKGROUND: International health experiences (IHEs) are popular among medical learners and provide a valuable learning experience. IHE participants have demonstrated an increased intention to care for underserved populations in the future, but what is its actual impact on practice? This study evaluates the effect of postgraduate IHE participation on the future careers of clinicians regarding their work among underserved populations. METHODS: We conducted a systematic review and meta-analysis of peer-reviewed articles comparing the populations served by physicians who had participated in an IHE with those of physicians who had not participated in an IHE. RESULTS: 764 titles were scanned, 28 articles were reviewed, with an eventual 3 studies of fair-good or good quality identified. These addressed physicians' service to domestic underserved populations, and also addressed future service in a low- or middle-income country (LMIC). Meta-analysis demonstrated a statistically-significant increase in service by IHE graduates to domestic underserved populations (OR = 2.12; CI = 95%; P = 0.03). The certainty of the evidence was low due to limitations in study design (non-randomised studies) and inconsistency in effects. CONCLUSION: Participation in an IHE may cause an increase in care for domestic underserved populations in future clinical practice, though further research from high quality randomised trials is needed to increase the certainty of the effect. Further study is needed to establish whether there is a similar effect with increased future service in a LMIC setting.
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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 | 0.022 | 0.052 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.050 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".