A writer's guide to education scholarship: Quantitative methodologies for medical education research (part 1)
Bibliographic record
Abstract
Background: Quantitative research is one of the many research methods used to help educators advance their understanding of questions in medical education. However, little research has been done on how to succeed in publishing in this area. Objective: We conducted a scoping review to identify key recommendations and reporting guidelines for quantitative educational research and scholarship. Methods: Medline, ERIC, and Google Scholar were searched for English-language articles published between 2006 and January 2016 using the search terms, "research design," "quantitative," "quantitative methods," and "medical education." A hand search was completed for additional references during the full-text review. Titles/abstracts were reviewed by two authors (BT, PC) and included if they focused on quantitative research in medical education and outlined reporting guidelines, or provided recommendations on conducting quantitative research. One hundred articles were reviewed in parallel with the first 30 used for calibration and the subsequent 70 to calculate Cohen's kappa coefficient. Two reviewers (BT, PC) conducted a full text review and extracted recommendations and reporting guidelines. A simple thematic analysis summarized the extracted recommendations. Results: Sixty-one articles were reviewed in full, and 157 recommendations were extracted. The thematic analysis identified 86 items, 14 categories, and 3 themes. Fourteen quality evaluation tools and reporting guidelines were found. Discussion This paper provides guidance for junior researchers in the form of key quality markers and reporting guidelines. We hope that quantitative researchers in medical education will be informed by the results and that further work will be done to refine the list of recommendations.
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 | 0.176 | 0.392 |
| Meta-epidemiology (narrow) | 0.004 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.023 | 0.025 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.009 | 0.009 |
| Research integrity | 0.008 | 0.018 |
| Insufficient payload (model declined to judge) | 0.047 | 0.039 |
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".