The era of virtual care: Perspectives of youth on psychiatric virtual appointments in COVID-19 and beyond
Bibliographic record
Abstract
Introduction In response to COVID-19, paediatric providers have shifted to providing outpatient health care appointments through telehealth. Objectives While research has been published previously on this topic, we felt it important to add current Canadian youth perspectives to the mix, specifically on changes due to COVID-19. Methods Semi-structured discussions were held on virtual care in June and October 2020 with our youth members, who are patients with various health conditions, aged 13 to 19 years which allowed us to glean their unique opinions regarding virtual care in the midst of a pandemic. Results Youth who contributed to this commentary reported that major benefits of virtual care included time savings, ease of access, continuity of care, and ability to participate in health appointments from the comfort of one’s own home without a risk of COVID-19 exposure. These youth also recognized limitations to virtual care, including the inability to complete laboratory or imaging tests, and the lack of physical examination capabilities. Conclusions Additionally, they stressed the importance of visual components of virtual appointments and health care providers needing to consider privacy restrictions youth may have. Overall, our cohort of youth feel positive about virtual care and hope care providers can work with youth individually to determine the best solution for them. Disclosure No significant relationships.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.008 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".