A Snapshot of Doctoral Training in Epidemiology: Positioning Us for the Future
Bibliographic record
Abstract
Although epidemiology core competencies are established by the Association of Schools and Programs of Public Health for masters-level trainees, no equivalent currently exists for the doctoral level. Thus, the objective of the Doctoral Education in Epidemiology Survey (2019) was to collect information on doctoral-level competencies in general epidemiology (doctoral) degree programs and other pertinent information from accredited programs in the United States and Canada. Participants (doctoral program directors or knowledgeable representatives of the program) from 57 institutions were invited to respond to a 39-item survey (18 core competencies; 9 noncore or emerging topic-related competencies; and 12 program-related items). Participants from 55 institutions (96.5%) responded to the survey, of whom over 85% rated 11 out of 18 core competencies as "very important" or "extremely important." More than 80% of the programs currently emphasize 2 of 9 noncore competencies (i.e., competency to ( 1) develop and write grant proposals, and ( 2) assess evidence for causality on the basis of different causal inference concepts). "Big data" is the most frequently cited topic currently lacking in doctoral curricula. Information gleaned from previous efforts and this survey should prompt a dialog among relevant stakeholders to establish a cohesive set of core competencies for doctoral training in epidemiology.
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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 | 0.015 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".