Conceptualising spaced learning in health professions education: A scoping review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVES: To investigate the definitions and applications of 'spaced learning' and to propose future directions for advancing its study and practice in health professions education. METHOD: The authors searched five online databases for articles published on spaced learning in health professions education prior to February 2018. Two researchers independently screened articles for eligibility with set inclusion criteria. They extracted and analysed key data using both quantitative and qualitative methods. RESULTS: Of the 2972 records retrieved, 120 articles were included in the review. More than 90% of these articles were published in the last 10 years. The definition of spaced learning varied widely and was often not theoretically grounded. Spaced learning was applied in distinct contexts, including online learning, simulation training and classroom settings. There was a large variety of spacing formats, ranging from dispersion of information or practice on a single day, to intervals lasting several months. Generally, spaced learning was implemented in practice or testing phases and rarely during teaching. CONCLUSIONS: Spaced learning is infrequently and poorly defined in the health professions education literature. We propose a comprehensive definition of spaced learning and emphasise that detailed descriptions of spacing formats are needed in future research to facilitate the operationalisation of spaced learning research and practice in health professions 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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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 it