The development of scales to measure childhood cancer survivors' readiness for transition to long‐term follow‐up care as adults
Bibliographic record
Abstract
PURPOSE: To develop and validate scales to measure constructs that survivors of childhood cancer report as barriers and/or facilitators to the process of transitioning from paediatric to adult-oriented long-term follow-up (LTFU) care. METHODS: Qualitative interviews provided a dataset that were used to develop items for three new scales that measure cancer worry, self-management skills and expectations about adult care. These scales were field-tested in a sample of 250 survivors aged 15-26 years recruited from three Canadian hospitals between July 2011 and January 2012. Rasch Measurement Theory (RMT) analysis was used to identify the items that represent the best indicators of each scale using tests of validity (i.e. thresholds for item response options, item fit statistics, item locations, differential item function) and reliability (Person Separation Index). Traditional psychometric tests of measurement performance were also conducted. RESULTS: RMT led to the refinement of a 6-item Cancer Worry scale (focused on worry about cancer-related issues such as late effects), a 15-item Self-Management Skills scale (focused on skills an adolescent needs to acquire to manage their own health care), and a 12-item Expectations scale (about the nature of adult LTFU care). Our study provides preliminary evidence about the reliability and validity of these new scales (e.g. Person Separation Index ≥ 0.81; Cronbach's α ≥ 0.81; test-retest reliability ≥ 0.85). CONCLUSION: There is limited knowledge about the transition experience of childhood cancer survivors. These scales can be used to investigate barriers survivors face in the process of transition from paediatric to adult care.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".