National Priorities for High-quality Rheumatology Transition Care for Youth in Canada
Bibliographic record
Abstract
OBJECTIVE: To conduct a needs assessment and environmental scan to support optimal transition from pediatric to adult rheumatology care in Canada. METHODS: This initiative involved 3 phases: (1) a survey-based needs assessment of adult and pediatric rheumatologist members of the Canadian Rheumatology Association to identify perceived infrastructure, educational needs, and national resources to support transition care; (2) an environmental scan, through semistructured interviews, of existing rheumatology transition service care models and challenges in care delivery; and (3) a focus group to prioritize national activities. RESULTS: The needs assessment survey was completed by 65 members, with 66% agreeing that a national approach to transition care was needed. Semistructured interviews reflecting activities at 9 transition care sites were conducted, and they identified candidate models of care, including direct transfer, progressive transfer, and shared care models. Challenges and needs experienced in these care models reflected resource and infrastructure needs, poor availability of mechanisms to support parents and youth through the transition process, and the need for evaluation to support quality improvement. The focus group and prioritization activity was attended by 26 participants, with each having the ability to cast 3 votes. "Supporting patient education for transition to adult rheumatology health care system" (n = 17 votes) and "advocacy activities to access allied health support, including funding" (n = 10 votes) emerged as the top priorities for national initiatives. CONCLUSION: We have identified priorities in education and advocacy for advancing transition care in Canada that require participation of pediatric and adult rheumatology providers, patients, and arthritis stakeholders in the interest of advancing transition care outcomes.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".