Is the <scp>G</scp>ive <scp>Y</scp>outh a <scp>V</scp>oice questionnaire an appropriate measure of teen‐centred care in paediatric oncology: a <scp>R</scp>asch measurement theory analysis
Bibliographic record
Abstract
BACKGROUND: Adolescents have their own views about the cancer care they receive and how they feel they are treated, but their opinions are rarely solicited. OBJECTIVE: To determine whether the 56-item Give Youth a Voice (GYV-56), its subscales and its 20-item short-form, are clinically meaningful and psychometrically sound instruments that can be used to measure teen-centred care (TCC) in paediatric oncology. DESIGN: Qualitative interviews and a questionnaire survey. SETTING AND PARTICIPANTS: Qualitative interviews with 38 childhood cancer survivors. GYV-56 data collected from 200 paediatric cancer patients and survivors. MAIN OUTCOME MEASURE: The GYV-56, which measures the following four aspects of service delivery: Supportive and respectful relationships; Information sharing and communication; Supporting independence; and Teen-centred services. RESULTS: Qualitative data provided broad support for the TCC conceptual framework and GYV-56 items. After post-hoc reduction of the response options from 7 to 3 (to correct for disordered thresholds), fit to the Rasch model was good, most items showed acceptable fit residuals and chi-square P-values, scale reliability were supported and item locations defined a continuum for TCC that was well-targeted to the sample. By calibrating the items for each subscale and the short-form to the full scale, the scores obtained on each measure are directly comparable. CONCLUSION: Our study found initial support for use of the GYV with a reduced response option format for examining TCC in the adolescent oncology patients. in this paediatric population. Further research using the GYV is needed to elaborate upon our findings.
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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 | 0.006 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".