Clinical support during COVID-19: An opportunity for service and learning? A cross-sectional survey of UK medical students
Bibliographic record
Abstract
Purpose Medical students providing support to clinical teams during Covid-19 may have been an opportunity for service and learning. We aimed to understand why the reported educational impact has been mixed to inform future placements.Methods We conducted a cross-sectional survey of medical students at UK medical schools during the first Covid-19 ‘lockdown’ period in the UK (March–July 2020). Analysis was informed by the conceptual framework of service and learning.Results 1245 medical students from 37 UK medical schools responded. 57% of respondents provided clinical support across a variety of roles and reported benefits including increased preparedness for foundation year one compared to those who did not (p < 0.0001). However, not every individual’s experience was equal. For some, roles complemented the curriculum and provided opportunities for clinical skill development, reflection, and meaningful contribution to the health service. For others, the relevance of their role to their education was limited; these roles typically focused on service provision, with few opportunities to develop.Conclusion The conceptual framework of service and learning can help explain why student experiences have been heterogeneous. We highlight how this conceptual framework can be used to inform clinical placements in the future, in particular the risks, benefits, and structures.Practice pointsThere was a benefit for most students who provided clinical support compared to those who did not during Covid-19.Most students found clinical support roles more beneficial than clinical placements and most final years wanted their final year clinical placements replaced by a formal role within a clinical team.Not every student’s experience of clinical support was equal. The conceptual framework of service and learning can help explain this heterogeneity.The most beneficial roles for students complemented the curriculum and provided opportunities for clinical skill development, reflective practice, and meaningful contribution to the health service.There is an added benefit of combining service and learning if done correctly, and we can use this to inform the structure of clinical placements going forwards. However, there are risks, and we discuss principles of good practice and provide our own considerations.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".