A92 PREVIOUS VIRTUAL CONSULTATION EXPERIENCE IS RELATED TO PRECEPTOR’S WILLINGNESS TO INVOLVE TRAINEES IN VIRTUAL CARE DURING THE COVID-19 PANDEMIC
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic has accelerated the need for healthcare service reform in order to reduce the risk of transmission of SARS-CoV-2 infection between patients, healthcare providers and medical trainees. Gastroenterologists have been challenged to adopt to virtual consultations and accommodate medical trainees in their virtual clinics. Aims To assess the impact of virtual care on medical education during the COVID-19 pandemic Methods A REDCap survey was disseminated among gastroenterology providers via email. The subsection of the survey on medical education consisted of 4 questions pertaining to inclusion of trainees in virtual clinics, type of virtual clinic, observation method and an open-ended question for additional comments. Quantitative data was analyzed using IBM SPSS Statistics 27 and qualitative theme analysis was applied for short answer responses. Results Of the 24 respondents that completed the survey, only 6 (25%) had trainees involved in their clinics (Table 1). The type of clinic consultations conducted were telephone only (50%), a combination of telephone, video and hospital-base telehealth (33.3%) and hospital-based telehealth only (16.7%). There was an equal split between direct and indirect observations. Preceptors that had previous experience with virtual consultation prior to the pandemic, were more likely to include trainees in their virtual clinics (66.6% vs 33.4%; Fisher’s exact test, p=0.033). For preceptors who included trainees in their virtual clinics, their overall satisfaction averaged 0.51 points lower (95% CI: 0.19–0.84, p=0.004). Concerns identified were lack of trainee engagement, adequate remuneration for healthcare providers, and lack of training for trainee and preceptors on how to navigate virtual platforms. Conclusions This survey demonstrates that gastroenterologists with previous experience with virtual clinics are more likely to accommodate trainees in their virtual clinics. However, involving trainees seem to reduce preceptor’s satisfaction with virtual clinic. Our findings suggest that there is a need to provide telemedicine training for both educators and trainees, in order to alleviate concerns and promote its adoption as organizations seek to continue to provide high-quality medical education while providing virtual care. Funding Agencies None
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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".