Improving Healthcare Transition of Young Adults with Chronic Diseases at The Ottawa Hospital: a cross-sectional study assessing the patient’s perspective on current practices
Bibliographic record
Abstract
Abstract Background: Transfer of young adults (YAs) with chronic diseases from pediatric- to adult hospital-based care is a major life event for this vulnerable group of patients. Inadequate preparation of the YAs and poorly planned transition of care are associated with subsequent treatment non-adherence, discontinuity of care and poor long-term outcomes. A formalized structured process for HCT has been demonstrated to improve patient experience and optimize subsequent engagement with adult healthcare and is now recommended as standard of care. Implementation of this approach is however suboptimal at a global level and practices vary within institutes and between specialties. We recognized this issue at the Ottawa Hospital (TOH) and sought to explore the impact of differences in approaches to HCT from the YA’s perspective. We assessed and compared patient experience of HCT in specialties with and without a structured HCT program with the aim of gaining insight into gaps in care to facilitate reforming current practices and optimizing care. Methods: YAs aged 18 to 25 years (n=175) who had transitioned into five adult specialties at TOH volunteered for this study. A self-care assessment survey and two feedback surveys were customized from the Got TransitionTM HCT measurement resources. Responses of those YAs attending clinics with (n=93) and without (n=82) a structured HCT program were assessed and compared.Results: YAs who transitioned into clinics with structured HCT reported better medical knowledge and practical skills for independent use of the healthcare system compared to YAs who attended clinics without a formal HCT process. This group reported a greater level of involvement in preparation for HCT by their pediatric health-care providers and better education and provision of practical information by their adult healthcare team compared to the YAs attending clinics without a formal HCT process. Conclusions: Results demonstrate superior health knowledge and healthcare usage skills in YAs attending clinics with structured HCT programs and support the benefit of establishing a structured approach to HCT across all specialties caring for transitioned YAs at TOH. By identifying strengths and gaps in current practices, this study has provided a basis to drive institutional reform to improve quality of care for this vulnerable patient population.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".