Transition readiness measures for adolescents and young adults with chronic health conditions: a systematic review
Bibliographic record
Abstract
Abstract Background Transition from pediatric to adult healthcare for adolescents with chronic health conditions has emerged as a critical period influencing health outcomes. Suitable transition readiness measures are necessary to facilitate effective planning. Currently, there is little consensus about well-validated transition readiness measures. The purpose of this systematic review was to identify best practices in transition readiness measurement for adolescents and young adults with chronic health conditions. Data sources Academic databases searched included PubMed, Cumulative Index to Nursing and Allied Health Literature, Google Scholar and Athabasca University Library’s Discover. Study selection Articles were included that discussed the development and psychometric properties of transition readiness measures for adolescents and young adults (11–25 years) with chronic health conditions and/or that utilized a previously developed measure. Data extraction The primary and secondary reviewers extracted data from the selected articles as per the data extraction tool developed for this review. Results Forty-eight articles, representing 19 tools, were included in the review. Ten of the tools were disease-specific; nine were disease-neutral. According to the Cohen criteria, eight measures were “well-established assessments”. The Transition Readiness Assessment Questionnaire was deemed the best measure of transition readiness available. The literature search included only articles published in peer-reviewed journals; measures nearing completion or awaiting publication were not included. In addition, only English manuscripts were included, many from North America. Conclusions Despite national practice guidelines and ongoing policy development, there is slow movement towards achieving a gold standard or best-practice measure of transition readiness.
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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 | 0.015 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".