P153 A patient-led survey into the benefits and limitations of telemedicine appointments for assessing children and young people with rheumatic conditions in Canada
Bibliographic record
Abstract
Abstract Background/Aims 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. Methods 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. 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). The majority of respondents reported telemedicine appointments had saved them time (68%), and many said it enabled them to have an appointment (63%) and made the appointment safer (59%), and many said it saved money (44%). However, 78% felt that their consultant could not properly assess their child, 22% were concerned that the doctor could not identify changes in their child’s condition, 14% said it was hard to explain their child’s condition, and 18% of parents and 22% of CYP disliked telemedicine. 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. 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. Disclosure J. Wilson: None. W. Costello: None. S. Angevare: None. R.P. Beesley: None.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".