A Provider-Facing eHealth Tool for Transitioning Youth With Special Health Care Needs From Pediatric to Adult Care: Mixed Methods, User-Engaged Usability Study
Bibliographic record
Abstract
BACKGROUND: There is a need for medical education on health care transitions for youth with special health care needs. The Texas Transition Toolkit (the tool) supports providers through a one-stop shop for researching literature on care transitions, a catalog of care transition tools, and guides for developing care transition programs. OBJECTIVE: This study aims to assess the functionality and usability of the tool with providers working with transition-aged children and youth with special health care needs (representative users). METHODS: The tool was evaluated using a triangulated mixed methods case study approach consisting of a concurrent think-aloud phase, a satisfaction survey, and a survey of problem relevance and task performance to operationalize and capture functionality and usability. Our mixed methods deep dive into the functionality and usability of the tool focused on 10 representative users from one medical home in Texas and 5 website design experts. RESULTS: Representative users found the tool to be highly relevant, as demonstrated by the satisfaction score for relevance (138/150, 92%). According to the users, the tool provided comprehensive information related to health care transitions for youth with special health care needs, with a satisfaction score of 87.3% (131/150) for comprehensive. Overall satisfaction with the tool was high at 81.92% (1065/1300) with a cutoff score of 73.33% (953.4/1300) indicating high satisfaction, but users reported relatively lower satisfaction with search (114/150, 76%) and navigation (ease of use: 114/150, 76%; hyperlinks: 163/200, 81.5%; structure: 159/200, 79.5%). They experienced search- and navigation-related problems (total problems detected: 21/31, 68%) and, based on quality checks, had a relatively low task completion rate for tasks involving finding information (60/80, 75%), which required searching and navigation. The problems identified around search and navigation functionality were relevant (relevance scores ranging from 14.5 to 22, with a cutoff score of 11.7 indicating relevance). CONCLUSIONS: The tool may help bridge the gaps in training on health care transitions for youth with special health care needs in US medical education. The tool can be used to create structured protocols to help improve provider knowledge, collaboration across pediatric and adult care providers, and the continuity of care as youth with special health care needs transition from pediatric to adult care. The results provided a road map for optimizing the tool and highlighted the importance of evaluating eHealth technologies with representative users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".