Seeking gastroenterological services during a pandemic: lessons from a large, national, population-based survey during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background The 2019 coronavirus pandemic (COVID-19) caused significant disruptions in people’s lives, healthcare-seeking behavior, and willingness to undergo endoscopic procedures. Methods This large national survey of adults used an online platform to collect participants' de-identified demographics, attitudes, and opinions regarding healthcare-seeking behavior and endoscopy during the COVID-19 pandemic. Data were analyzed using descriptive statistics and multivariate logistic regression. Results There were 29 449 respondents; mean age 43.3 ± 17.3 years, 72% female. Among 3928 respondents who visited their doctor virtually during the COVID-19 pandemic, most were satisfied (76%). In a multivariate analysis, respondents who were satisfied or neutral toward a virtual visit were more likely to be married, African American, and have some college education. In contrast, those who were dissatisfied were more likely to be older or female. Only 26.3% (n = 7746) reported concerns about undergoing endoscopy during the pandemic. Among those respondents, preferences were to reschedule 3-4 months later (38%) after having had the vaccine (19%) or to forgo the procedure entirely (28%). In a multivariate analysis, having had a prior endoscopy was most strongly associated with concern, followed by African American race, female sex, being married, and older age. Conclusion This large national survey suggested high satisfaction with virtual visits and low concern about undergoing endoscopy during the pandemic. Concern around endoscopy was increased among those who had a prior endoscopy, African Americans, women, and older age groups.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".