Leveraging a Virtual Community of Practice to Participate in a Survey‐based Study: A Description of the METRIQ Study Methodology
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it