Interventions for undergraduate and postgraduate medical learners with academic difficulties: A BEME systematic review update: BEME Guide No. 85
Bibliographic record
Abstract
Background Clinical teachers often struggle to record trainee underperformance due to lacking evidence-based remediation options.Objectives To provide updated evidence-based recommendations for addressing academic difficulties among undergraduate and postgraduate medical learners.Methods A systematic review searched databases including MEDLINE, CINAHL, EMBASE, ERIC, Education Source, and PsycINFO (2016–2021), replicating the original Best Evidence Medical Education 56 review strategy. Original research/innovation reports describing intervention(s) for medical learners with academic difficulties were included. Data extraction used Michie’s Behaviour Change Techniques (BCT) Taxonomy and program evaluation models from Stufflebeam and Kirkpatrick. Quality appraised used the Mixed Methods Appraisal Tool (MMAT). Authors synthesized extracted evidence by adapting GRADE approach to formulate recommendations.Results Eighteen articles met the inclusion criteria, primarily addressing knowledge (66.7%), skills (66.7%), attitudinal problems (50%) and learner’s personal challenges (27.8%). Feedback and monitoring was the most frequently employed BCT. Study quality varied (MMAT 0–100%). We identified nineteen interventions (UG: n = 9, PG: n = 12), introducing twelve new thematic content. Newly thematic content addressed contemporary learning challenges such as academic procrastination, and use of technology-enhanced learning resources. Combined with previous interventions, the review offers a total dataset of 121 interventions.Conclusion This review offers additional evidence-based interventions for learners with academic difficulties, supporting teaching, learning, faculty development, and research efforts.
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 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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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; both teacher heads 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".