Differences in Healthcare Transition Views, Practices, and Barriers Among North American Pediatric Rheumatology Clinicians From 2010 to 2018
Bibliographic record
Abstract
Objective Since 2010, the rheumatology community has developed guidelines and tools to improve healthcare transition. In this study, we aimed to compare current transition practices and beliefs among Childhood Arthritis and Rheumatology Research Alliance (CARRA) rheumatology providers with transition practices from a provider survey published in 2010. Methods In 2018, CARRA members completed a 25-item online survey about healthcare transition. Got Transition’s Current Assessment of Health Care Transition Activities was used to measure clinical transition processes on a scale of 1 (basic) to 4 (comprehensive). Bivariate analyses were used to compare 2010 and 2018 survey findings. Results Over half of CARRA members completed the survey (202/396), including pediatric rheumatologists, adult- and pediatric-trained rheumatologists, pediatric rheumatology fellows, and advanced practice providers. The most common target age to begin transition planning was 15–17 years (49%). Most providers transferred patients prior to age 21 years (75%). Few providers used the American College of Rheumatology transition tools (31%) or have a dedicated transition clinic (23%). Only 17% had a transition policy in place, and 63% did not consistently address healthcare transition with patients. When compared to the 2010 survey, improvement was noted in 3 of 12 transition barriers: availability of adult primary care providers, availability of adult rheumatologists, and pediatric staff transition knowledge and skills (P < 0.001 for each). Nevertheless, the mean current assessment score was < 2 for each measurement. Conclusion This study demonstrates improvement in certain transition barriers and practices since 2010, although implementation of structured transition processes remains inconsistent.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".