Implementation of an inter‐agency transition model for youth with spina bifida
Bibliographic record
Abstract
BACKGROUND: To address gaps in transfer of care and transition support, a paediatric hospital and adult community health care centre partnered to implement an inter-agency transition model for youth with spina bifida. Our objective was to understand the enablers and challenges experienced in the implementation of the model. METHODS: Using a descriptive, qualitative design, we conducted semi-structured interviews, in-person or over the phone, with 12 clinicians and nine key informants involved in implementing the spina bifida transition model. We recruited all 21 participants from an urban area of Ontario, Canada. RESULTS: Clinicians and key informants experienced several enablers and challenges in implementing the spina bifida transition model. Enablers included dedicated leadership, advocacy, funding, inter-agency partnerships, cross-appointed staff and gaps in co-ordinated care to connect youth to adult services. Challenges included gaps in the availability of adult specialty services, limited geographical catchment of adult services, limited engagement of front-line staff, gaps in communication and role clarity. CONCLUSIONS: Although the transition model has realized some initial successes, there are still many challenges to overcome in transferring youth with spina bifida to adult health care and transitioning to adulthood.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".