Educator's blueprint: Key considerations for using social media in survey‐based medical education research
Bibliographic record
Abstract
In this paper, we present a set of recommendations for using social media as a tool for participant recruitment in survey-based medical education research. Drawing from a limited but growing body of literature, we discuss the opportunities and challenges inherent to social media recruitment. This article builds on the authors' previous educator's blueprints about survey design and administration. We highlight the advantages of social media, including its wide reach, cost-effectiveness, and capability to access diverse and geographically dispersed populations, which can significantly enhance the representativeness of research samples. However, we also caution against potential pitfalls, such as ethical concerns, sampling bias, and the fluid nature of social media platforms. Our recommendations are informed by both empirical evidence and best practices, aiming to provide researchers with practical advice for effectively leveraging social media in survey-based medical education research. We emphasize the importance of selecting suitable platforms and engaging with targeted demographics thoughtfully. By sharing our insights, we hope to assist fellow medical education researchers in navigating the complexities of social media recruitment, thereby enriching the quality and impact of survey-based research in this field.
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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 | 0.596 | 0.814 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.018 |
| Scholarly communication | 0.020 | 0.024 |
| Open science | 0.007 | 0.014 |
| Research integrity | 0.031 | 0.048 |
| Insufficient payload (model declined to judge) | 0.008 | 0.011 |
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".