P.077 Telemedicine in pediatric neurology: a survey of patient and provider experience
Bibliographic record
Abstract
Background: Prior to the pandemic, telemedicine use was limited and sparsely funded within Ontario. During the pandemic, a shift in clinical recommendations and government funding models promoted telemedicine. We aim to highlight both quantitative and qualitative aspects of the patient and provider experience over 2.5 years within a Canadian Pediatric Neurology clinic. Main objectives of the study are to assess the safety, efficiency and convenience of telemedicine. Methods: A REDCap survey was sent to all patients with a telemedicine appointment from March 2020 –September 2022 and all Pediatric Neurology providers. Survey included a 5-point Likert scale questions, open questions, and patient characteristics. Results: Responses received from 272 patients and 7 providers. 91% of patients and all providers were satisfied with telemedicine. 95% of patients and all providers felt they received or were able to provide safe/adequate care. 90% of patients and all providers reported that telemedicine was more convenient. 87% of patients and all providers were interested in future appointments via telemedicine. Conclusions: Our survey shows patients and providers had highly positive experiences with telemedicine – reporting care was adequate, safe, and more convenient. This data supports incorporating telemedicine into future care and advocates that Canadian regulations/billing codes to continue to support telemedicine.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".