Conducting a best evidence systematic review. Part 1: From idea to data coding. BEME Guide No. 13
Bibliographic record
Abstract
This paper outlines the essential aspects of conducting a systematic review of an educational topic beginning with the work needed once an initial idea for a review topic has been suggested through to the stage when all data from the selected primary studies has been coded. It draws extensively on the wisdom and experience of those who have undertaken systematic reviews of professional education, including Best Evidence Medical Education systematic reviews. Material from completed reviews is used to illustrate the practical application of the review processes discussed. The paper provides practical help to new review groups and contributes to the debate about ways of obtaining evidence (and what sort of evidence) to inform policy and practice in education.
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 | 0.293 | 0.463 |
| Meta-epidemiology (narrow) | 0.004 | 0.007 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.020 | 0.021 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.046 | 0.025 |
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".