P62 The benefits and limitations of telemedicine appointments for assessing children and young people with rheumatic conditions in Canada: A patient-led survey
Bibliographic record
Abstract
Abstract Introduction/Background During the COVID-19 (coronavirus) pandemic, some healthcare provision shifted to remote, technology-assisted appointments (telemedicine). This study sought the views of parents/carers about telemedicine, identifying the benefits and limitations, to assist in improvement to future service provision. Description/Method An online survey was developed and shared via social media and direct contacts, targeted at parents of children with rheumatic and autoinflammatory conditions in Canada. Fieldwork took place during May 2021. Consent was provided during enrolment. Discussion/Results A total of 157 responses were received (78% female, median age 12). The primary diagnosis for the majority was Juvenile Idiopathic Arthritis (JIA; 39% polyarticular, 15% oligoarticular, 8% enthesitis-related JIA, 6% psoriatic, and 9% systemic). Respondents reported in-person appointments represent a considerable time burden (87% travel more than an hour to attend; 40% take a full day (or more) out of school to attend; 38% of parents take a full day off work). During the pandemic, the proportion having a telemedicine appointment increased from 5% to 82%. Table 1 shows the scores (1 worst, 5 best) given by parents about their telemedicine experience. Overall, most aspects scored positively (p<.05). However, parents felt telemedicine was not as good as in-person appointments (mean 2.66, 95% CI 2.42-2.90). Overall 61% said they would prefer the next appointment to be in-person, while 31% were amenable to some combination of in-person and virtual care. Key learning points/Conclusion There are advantages to telemedicine, notably saving time and making appointments accessible, and overall parents reported satisfaction with remote appointments. However, parents continue to report the value of in-person appointments.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".