Experience and Impact of COVID-19 on a Newly Formed Rural University Medical Office: Survey Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 pandemic had large social effects, particularly in the fields of medicine and medical education. Medical organizations in the United States operate in overlapping contexts with interrelated goals inside multiple organizations, and the context of work strongly influenced how organizations were able to respond to COVID-19 restrictions. OBJECTIVE: This research examines the experience and impact of COVID-19 on the implementation of a Health Resources and Services Administration grant in a newly formed university medical office with the interrelated goals of health policy, health outreach, and medical education. The goal is to understand how COVID-19 created different experiences and challenges for leaders and their collaborators working in medical education compared to those working in public health outreach or health policy. METHODS: A survey about COVID-19 opportunities and challenges was administered to work unit leaders and their project collaborators. The most common experiences and challenges are shown, direct educational and other respondents' experiences and challenges are compared, and open-ended comment segments are analyzed. RESULTS: Helping others adjust to digital work, remoteness, and coordination were common experiences during COVID-19. Common challenges include coordination and an inability to make comparisons to previous program years. On average, respondents had 11.3 (SD 7.8) experiences and 8.3 (SD 6.9) challenges considered in the survey. While all units were influenced by COVID-19 restrictions, medical education units had more experiences and challenges. Those involved directly in medical education experienced 69% (18.6/27) of their possible experiences and 54% (14.7/27) of their possible challenges on average compared to 35% (7/20) and 21% (4.2/20) among other respondents (P<.001). COVID-19 restrictions increased the complexity of project work and presented challenges, especially in terms of coordinating responses and access to locations. CONCLUSIONS: The findings suggest that COVID-19 made the overall administration of programs more complex and drew attention from other medical and public health programs. While remoteness is appropriate for some medical education tasks, it is less appropriate for clinical learning. Remoteness presents an especially large challenge to clinical education. Employees now have expectations for remoteness to be built into programs and workplaces. Program administrators will have to integrate remoteness' benefits and drawbacks into their organization for the foreseeable future.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".