Development and Validation of a Ready-to-Talk Measure for Use in Adolescents and Young Adults Living With Advanced Cancer
Bibliographic record
Abstract
BACKGROUND: In the era of evolving and emerging therapies, adolescents and young adults (AYAs) living with advanced cancer experience a high degree of uncertainty, making palliative care and end-of-life (PCEOL) discussions difficult. Clinical conversations determine values/preferences that guide shared decision-making and goals of treatment, including end-of-life care when cancer progresses. Initiating PCEOL conversations is challenging for clinicians. OBJECTIVE: This study describes the development and validation of an instrument that measures AYA readiness to engage in PCEOL clinical conversations. METHODS: A Ready-to-Talk Measure (R-T-M) was developed, guided by the revised conceptual model of readiness across 3 domains (awareness, acceptance, and willingness). Content experts evaluated validity, and 13 AYAs with advanced cancer participated in cognitive interviews. Acceptability (item applicability, clarity, interpretation, sensitivity, missingness) and experiences (benefit, burden) were analyzed. RESULTS: The scale content validity index was ≥0.90 for each domain. Forty-two of the 55 R-T-M items were acceptable without any change. Three items were deleted. Ten items were modified, and 3 were added. Adolescents and young adults wanted more items about friends/siblings and about AYA unique qualities for clinicians to know them better. Adolescents and young adults acknowledged benefit through talking about difficult, relevant topics. CONCLUSION: Ready-to-Talk Measure validity was strengthened by deleting or modifying unclear or misinterpreted items and by adding items. Next steps include psychometric analysis to determine reliability/dimensionality and stakeholder input to make the R-T-M a clinically useful tool. IMPLICATIONS FOR PRACTICE: Ready-to-Talk Measure assessment of readiness to engage in PCEOL conversations while identifying unique preferences of AYAs holds promise for facilitating ongoing discussions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".