Journal recommended guidelines for survey‐based research
Bibliographic record
Abstract
Survey-based research is vital in education and social sciences, offering insights into human behaviors and perceptions. The prevalence of such studies in medical education has risen by 33% over the past decade. Despite this growth, the utility of survey findings depends on the study design quality and measure validity. Many manuscripts are rejected due to poor planning and lack of validity evidence. These guidelines aim to improve the rigor and reporting of survey-based research, ensuring credibility and reproducibility. They apply to various survey tools and evaluations, setting a standard for manuscript quality and informing the review process for Anatomical Science Education.
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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 | 0.103 | 0.379 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.011 | 0.010 |
| Bibliometrics | 0.037 | 0.035 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.189 | 0.116 |
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".