Undergraduate and graduate student training in global health research: Preparing the next generation
Bibliographic record
Abstract
Structure/Method/Design: For the period from January 2012 to December 2013, multidisciplinary teams that included medical students and residents from hospitals in Canada and the United States, mobilized bimonthly, and conducted outreach clinics in five rotating communities seeing 80 to 160 children daily for 1 week.All participants were required to have predeparture training and protocols were developed to ensure consistent diagnosis and treatment of common conditions in children.Statistics were collected for each day and location. Results (Scientific Abstract)/Collaborative Partners (Programmatic Abstract): Haiti Village HealthHaitian Ministry of Health Summary/Conclusion: The data obtained for the years 2012 and 2013 were compared for all children seen from newborn to age 15 years.There were five outreach programs from four different hospital and residency programs in each year conducted in the same months each year.There was an equal amount of patients served in both years with a similar sex and age distribution.The data for the top 12 most common diagnoses were reviewed and compared over this 2-year period.A total 4825 distinct diagnosis in 4133 patients were reviewed and compared.There was no significant difference found between the rate of recorded diagnoses in the years 2012 and 2013.Results were consistent across all diagnostic categories and independent of team composition.A protocol-based global child health approach is effective at improving the accuracy of diagnosis and treatment among medical trainees of varying experience and from multiple training programs.
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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 | 0.035 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".