A systematic survey of methods guidance suggests areas for improvement regarding access, development, and transparency
Bibliographic record
Abstract
BACKGROUND: To assess the current practice of developing and presenting methods guidance and explore opportunities for improvement. STUDY DESIGN AND SETTING: We systematically surveyed methods guidance published in high-impact general and methodology-focused medical journals indexed in MEDLINE in 2020. We included articles that explicitly stated the objective to provide methods guidance for health research. We extracted characteristics related to findability, methods used for development, presentation, and transparency. RESULTS: We included 105 methods guidance articles published in 12 different journals. Less than half had a structured abstract (42%) or was indexed with medical subject headings (38%) or author keywords (17%) related to guidance. Methods for development, reported in 42%, differed between reporting guidelines (n = 13, 100% reported methods) and other guidance articles (n = 92, 34% reported methods). Frequent methods for presentation were illustrative case studies (45%), research checklists (34%), and step-by-step guides (10%). Most articles did not describe the authors' expertise (22%). Conflicts of interest, reported in 34%, were often unclear. CONCLUSION: Potential areas for improving methods guidance include better findability through more consistent labeling and indexing and standards for development and reporting.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.549 | 0.814 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.032 | 0.033 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".