Global Health Experience and Interest: Results From the AAP Periodic Survey
Bibliographic record
Abstract
BACKGROUND: Interest and participation in global health (GH) experiences have increased over the past 30 years in both medical schools and residencies, but little is known at the level of practicing pediatricians. METHODS: Data were compared from the American Academy of Pediatrics Periodic Surveys conducted in 1989 and 2017. The surveys had a response rate of 70.8% in 1989 and 46.7% in 2017. There were 638 and 668 postresidency pediatricians in the 1989 and 2017 surveys, respectively. Descriptive analyses were performed to look at changes in experience and interest in GH. A multivariable logistic regression was conducted specifically looking at characteristics associated with interest in participating in GH experiences in the next 3 years. RESULTS: Pediatrician participation in GH experiences increased from 2.2% in 1989 to 5.1% in 2017, with statistically significant increases in pediatricians ≥50 years of age. Interest in participating in future GH experiences increased from 25.2% in 1989 to 31.7% in 2017, with a particular preference for short-term clinical opportunities. In the multivariable logistic regression model, the year 2017 was associated with an increased interest in future GH experience, especially in medical school, hospital or clinic practice settings, as well as among subspecialists. CONCLUSIONS: Over the past 28 years, practicing pediatricians have increased their involvement in GH, and they are more interested in future GH experiences. The focus is on short-term opportunities. Our study reveals that practicing pediatricians mirror medical trainees in their growing interest and participation in GH.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".