Changes in medical student attendance and its impact on student educational outcomes: a systematic review protocol
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic has had a significant impact on medical education, with many institutions shifting to online learning to ensure the safety of students and staff. However, there has been a decline in in-person attendance at medical schools across the UK and worldwide following the relaxation of social distancing rules and the reinstation of in-person teaching. Importantly, this trend has been observed prior to the pandemic. While reflected within the literature, there is currently no systematic review describing these changes. We aim to find out how medical students' attendance is changing as documented within the literature and its impact on their educational outcomes. METHODS AND ANALYSIS: This systematic review will follow the guidelines of the Centre of Research and Dissemination, Meta-analyses of Observational Studies in Epidemiology and Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We will search the major databases of Medline via Ovid, Embase via Ovid, Scopus, Web of Science, British Education Index via EBSCOhost and ERIC via EBSCOhost.Two reviewers will independently screen each paper and extract data, with a third reviewer for dispute resolution. All studies reporting on medical students from various universities, both graduate and undergraduate and describing changes in attendance and/or students' educational outcomes will be included. Risk of bias in individual studies will be assessed using the Newcastle-Ottawa Scale and confidence in cumulative evidence will be evaluated using the Grading of Recommendations, Assessment, Development and Evaluation-Confidence in the Evidence from Reviews of Qualitative Research approach. A narrative synthesis of the findings from all included studies will be reported. ETHICS AND DISSEMINATION: Ethical approval is not required for this systematic review of existing publicly available literature. We will subsequently aim to publish the results of this systematic review in a peer-reviewed journal.
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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 | 0.103 | 0.087 |
| Meta-epidemiology (narrow) | 0.007 | 0.007 |
| Meta-epidemiology (broad) | 0.024 | 0.018 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.009 | 0.007 |
| Insufficient payload (model declined to judge) | 0.077 | 0.012 |
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".