Implementation of Rheumatology Health Care Transition Processes and Adaptations to Systems Under Stress: A <scp>Mixed‐Methods</scp> Study
Bibliographic record
Abstract
OBJECTIVES: Despite poor health care transition outcomes among young adults with pediatric rheumatic diseases, adoption of transition best practices is low. We sought to understand how structured transition processes were operationalized within pediatric rheumatology practices and what factors were perceived to enable adaptations during a global pandemic. METHODS: We conducted a mixed methods study of team leaders' experiences during an interim analysis of a pilot project to implement transition policy discussions at sites in the Childhood Arthritis and Rheumatology Research Alliance Transition Learning Collaborative. We combined quantitative assessments of organizational readiness for change (9 sites) and semistructured interviews of team leaders (8 sites) using determinants in the Exploration, Preparation, Implementation, Sustainment Framework. RESULTS: Engagement of nursing and institutional improvement efforts facilitated decisions to implement transition policies. Workflows incorporating educational processes by nonphysicians were perceived to be critical for success. When the pandemic disrupted contact with nonphysicians, capacity for automation using electronic medical record (EMR)-based tools was an important facilitator, but few sites could access these tools. Sites without EMR-based tools did not progress despite reporting high organizational readiness to implement change at the clinic level. Lastly, educational processes were often superseded by acute issues, such that youth with greater medical/psychosocial complexity may not receive the intervention. CONCLUSION: We generated several considerations to guide implementation of transition processes within pediatric rheumatology from the perspectives of team leaders. Careful assessment of institutional and nursing support is advisable before conducting complex transition interventions. Ideally, new strategies would ensure interventions reach youth with high complexity.
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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 | 0.042 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".