Leveraging a Virtual Community of Practice to Participate in a Survey‐based Study: A Description of the METRIQ Study Methodology
Bibliographic record
Abstract
OBJECTIVES: To power the METRIQ (Medical Education Translational Resources: Impact and Quality) Study adequately, we aimed to recruit > 200 medical students, residents, and attendings to complete a 90- to 120-minute survey by leveraging a virtual community of practice (vCoP). METHODS: Participants were recruited using personal (conference campaign and e-mails) and online (a study website and social media campaign utilizing Twitter, Facebook, blogs, podcasts, an infographic, and a YouTube video) techniques that leveraged relationships within a virtual community or practice. Participants received weekly survey reminders for 4 weeks and at the end of the rating period. Survey completion rates were calculated. RESULTS: A total of 380 potential participants completed an intake form (139 medical students, 120 residents, 121 attendings), 330 consented to participate, and 309 (81.3% of interested and 93.9% of consenting participants) completed the full survey (121, 88, and 100, respectively). The required sample size was achieved. CONCLUSIONS: The METRIQ Study utilized a multimodal recruitment campaign that targeted a vCoP. It recruited large numbers of participants with high completion rates. Response rates could not be calculated given the uncertainty surrounding the number of individuals invited to participate.
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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 | 0.073 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".