Landscape of healthcare transition services in Canada: a multi-method environmental scan
Bibliographic record
Abstract
BACKGROUND: Poorly supported transitions from pediatric to adult healthcare can lead to negative health outcomes for youth and their families. To better understand the current landscape of healthcare transition care across Canada, the Canadian Health Hub in Transition (the "Transition Hub", established in 2019) identified a need to: (1) describe programs and services supporting the transition from pediatric to adult healthcare across Canada; and (2) identify strengths, barriers, and gaps affecting the provision of transition services. METHODS: Our project included two iterative steps: a national survey followed by a qualitative descriptive study. Service providers were recruited from the Transition Hub and invited to complete the survey and participate in the qualitative study. The survey was used to collect program information (e.g., setting, clinical population, program components), and semi-structured interviews were used to explore providers' perspectives on strengths, barriers, and gaps in transition services. Qualitative data were analyzed using the Framework Method. RESULTS: Fifty-one surveys were completed, describing 48 programs (22 pediatric, 19 bridging, and 7 adult) across 9 provinces. Almost half of the surveyed programs were in Ontario (44%) and most programs were based in hospital (65%) and outpatient settings (73%). There was wide variation in the ages served, with most programs focused on specific diagnostic groups. Qualitative findings from 23 interviews with service providers were organized into five topics: (1) measuring transition success; (2) program strengths; (3) barriers and gaps; (4) strategies for improvement; and (5) drivers for change. CONCLUSIONS: While national transition guidelines exist in Canada, there is wide variation in the way young people and their families are supported. A national strategy, backed by local leadership, is essential for instigating system change toward sustainable and universally accessible support for healthcare transition in Canada.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.017 | 0.044 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.001 | 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".