Bibliographic record
Abstract
The expectation that the primary function of systematic reviews in medical education is to guide the development of professional practice requires basic standards to make the reports of these reviews more useful to evidence-based practice and to allow for further meta-syntheses. However, medical education research is a field rather than a discipline, one that brings together multiple methodological and philosophical approaches and one that struggles to establish coherence because of this plurality. Gordon and Gibbs have entered the fray with their common framework for reporting systematic reviews in medical education independent of their theoretical or methodological focus, which raises questions regarding the specificity of medical education research and how their framework differs from other systematic review reporting frameworks. The STORIES (STructured apprOach to the Reporting In healthcare education of Evidence Synthesis) framework will need to be tested in practice and potentially it will need to be adjusted to accommodate emerging issues and concerns. Nevertheless, as systematic reviews fulfill a greater role in evidence-based practice then STORIES or its successors should provide an essential infrastructure through which medical education syntheses can be translated into medical education practice. Please see related article: http://www.biomedcentral.com/1741-7015/12/143.
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 | 0.695 | 0.904 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.025 | 0.020 |
| Science and technology studies | 0.009 | 0.024 |
| Scholarly communication | 0.033 | 0.036 |
| Open science | 0.016 | 0.033 |
| Research integrity | 0.021 | 0.025 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".