The Critical Importance of Asking Good Questions: The Role of Epidemiology Doctoral Training Programs
Bibliographic record
Abstract
Epidemiologic methods have advanced tremendously in the last several decades. As important as they are, even the most sophisticated approaches are unable to provide meaningful answers when the user lacks a clear study question. Yet, instructors have more and more resources on how to conduct studies and analyze data but few resources on how to ask clearly defined study questions that will guide those methods. Training programs have limited time for coursework, and if novel statistical estimation methods become the focus of instruction, programs that go this route may end up underemphasizing the process of asking good study questions, designing robust studies, considering potential biases in the collected data, and appropriately interpreting the results of the analysis. Given the demands for space in curricula, now is an appropriate time to reevaluate what we teach epidemiology doctoral students. We advocate that programs place a renewed focus on asking good study questions and following a comprehensive approach to study design and data analysis in which questions guide the choice of appropriate methods, helping us avoid methods for methods' sake and highlighting when application of a new method can provide the opportunity to answer questions that were intractable with traditional approaches.
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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 | 0.423 | 0.546 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.019 | 0.050 |
| Scholarly communication | 0.024 | 0.032 |
| Open science | 0.005 | 0.031 |
| Research integrity | 0.017 | 0.047 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".