Implementing transition readiness screening by nurses in pediatric oncology long-term follow-up care during the COVID-19 pandemic: Barriers and facilitators
Bibliographic record
Abstract
This study aimed to 1) describe adolescent cancer survivors' transition readiness and self-reported goals in a limited clinical sample; and 2) investigate healthcare providers' (HCPs') perspectives about a nurse-led clinical utilization of the Transition Readiness Assessment Questionnaire (TRAQ) and its subsequent discontinuation in a long-term follow-up (LTFU) pediatric oncology clinic. Data were collected from adolescent survivors (n = 7) at a tertiary pediatric hospital, while in-depth interviews were conducted with HCPs (n = 3) from the LTFU oncology clinic. Qualitative data revealed barriers, facilitators, and strategies for TRAQ implementation, relating to the tool, HCPs, adolescents, and the clinical context. The understanding of the barriers and facilitators to TRAQ use will support a structured implementation plan for TRAQ use in future clinical practice. Analysis revealed varying levels of readiness for transition to adult care services among patients and identified themes in their self-set goals, supporting the need for transition readiness supports in this 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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".