Bibliographic record
Abstract
Over the last 20 years, the volume of publications on topics in health professions education (HPE) has increased dramatically. To help the HPE community integrate some of these multiple studies, researchers are increasingly creating, consuming, and citing knowledge syntheses. This chapter defines and describes the characteristics of a knowledge synthesis. It situates knowledge syntheses in the context of HPE, including a discussion of how they may be used and the five types of knowledge syntheses that are prevalent in, or appropriate for use by, those in HPE. These are narrative reviews, systematic reviews, umbrella reviews (aka meta-syntheses), scoping reviews, and realist reviews. The chapter outlines a seven-step process for those seeking to undertake knowledge syntheses. They are defining a focused research question, determining knowledge synthesis type, recruiting the research team, identifying materials for inclusion, extracting key data, analyzing and synthesizing results, and reporting. Lastly, the chapter explores the available training for knowledge syntheses in HPE.
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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 | 0.123 | 0.500 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.029 | 0.019 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.166 | 0.028 |
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".