Using Mixed Methods to Facilitate Complex, Multiphased Health Research
Bibliographic record
Abstract
From conceptualization to application and evaluation, research is conducted in a context of increasing complexity of disciplines, goals, communities, and partnerships. Researchers often are challenged to demonstrate the rigor of their methods and results to audiences with diverse backgrounds and disciplinary expertise. This article illustrates the benefits of using mixed methods approaches in research designed to address issues in complex research projects. It outlines the implementation of a private, public, and academic partnership, where scientific merit of methods and results was a critical foundation to the development of public policy. The overall goal of the Public Health Leadership Competencies Project (the Project) was to identify public health leadership competencies that could apply to public health practice across the country. This research demonstrates how mixed methods research in public health might be of perceived benefit to complex projects. The Project included challenges and opportunities through multiple phases of data collection and participation of members from each of the seven disciplines in public health (i.e., public health dentists, physicians, dietitians, and nurses, as well as epidemiologists, health promoters, and environmental health inspectors). The discussion addresses challenges of a national project, the complex organizational framework within which we were directed to work, and the lessons associated with using multiple sources of data.
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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 | 0.369 | 0.311 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.015 | 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; 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".