Comparing the suitability of virtual versus in-person care: Perceptions from pediatricians
Bibliographic record
Abstract
ObjectivesThe COVID-19 pandemic compelled a portion of healthcare to be delivered virtually. As the pandemic waned, health systems strived to find a balance between re-incorporating in-person care while maintaining virtual care. To find when virtual or in-person encounters are more appropriate, we surveyed pediatricians' perceptions when comparing the suitability of virtual care to in-person care.MethodsWe surveyed a Canadian tertiary-level pediatric hospital where pediatricians assessed whether specific clinical encounters or tasks were more or less effective virtually than when performed in person. Pediatricians also rated the importance of clinical and patient factors when deciding if a patient needs to be seen in person.ResultsOf 160 pediatrics faculty members, 56 (35%) responded to the survey. When assessing different types of clinical encounters, triage, multidisciplinary meetings, discharge, and follow ups were more likely to favor virtual encounters. However, first consultations and family meetings were more likely to favor in-person encounters. Regarding clinical tasks, pediatricians were more likely to endorse explaining test results, offering treatment recommendations, and obtaining patient histories virtually. On the contrary, there was a preference for physical examinations, assessing patients visually, and assessing developmental milestones to be performed in person. When deciding if a patient should be seen in person versus virtual, pediatricians rated the patient's condition and communication barriers as the most important factors favoring an in-person appointment.DiscussionThese results offer an initial framework for pediatricians when choosing which encounter type may be most appropriate for their patients between virtual or in-person appointments.
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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".