Examining Medical Student Volunteering During The COVID-19 Pandemic As A Prosocial Behavior During An Emergency
Bibliographic record
Abstract
ABSTRACT Introduction COVID-19 has caused major disruptions to healthcare, with voluntary opportunities offered to medical students to provide clinical support. We used the conceptual framework of prosocial behavior during an emergency – behaviors whose primary focus is benefiting others – to examine volunteering during COVID-19. Methods We conducted an in-depth, mixed-methods cross-sectional survey, from 2 nd May to 15 th June 2020, of medical students studying at UK medical schools. Data analysis was informed by Latane and Darley’s theory of prosocial behavior during an emergency and aimed to understand students’ decision-making processes. Results A total of 1145 medical students from 36 medical schools completed the survey. While 947 (82.7%) of students were willing to volunteer, only 391 (34.3%) had volunteered. The majority (92.7%) of students understood that they may be asked to volunteer; however, we found that deciding one’s responsibility to volunteer was mitigated by a complex interaction between the interests of others and self-interest. Further, concerns revolving around professional role boundaries influenced students’ decisions over whether they had the required skills and knowledge to volunteer. Deciding to volunteer depended not only on possession of necessary skills, but also seniority and identification with the nature of volunteering roles offered. Conclusions We propose two additional domains to Latane and Darley’s theory of prosocial behavior during an emergency that students consider before making their final decision to volunteer. These are ‘logistics’ – whether it is logistically feasible to volunteer – and ‘safety’ – whether it is safe to volunteer. This study highlights a number of modifiable barriers to prosocial behavior that medical students encounter and provides suggestions regarding how Latane and Darley’s theory of prosocial behavior can be operationalized within educational strategies to address these barriers. Optimizing the process of volunteering can aid healthcare provision and may facilitate a safer volunteering process for all.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".