Thinking globally to improve care locally: A Delphi study protocol to achieve international clinical consensus on best-practice end-of-life communication with adolescents and young adults with cancer
Bibliographic record
Abstract
For the sizeable subset of adolescents and young adults whose cancer is incurable, developmentally appropriate end-of-life discussions are critical. Standards of care for adolescent and young adult end-of-life communication have been established, however, many health-professionals do not feel confident leading these conversations, leaving gaps in the implementation of best-practice end-of-life communication. We present a protocol for a Delphi study informing the development and implementation of clinician training to strengthen health-professionals' capacity in end-of-life conversations. Our approach will inform training to address barriers to end-of-life communication with adolescents and young adults across Westernized Adolescent and Young Adult Cancer Global Accord countries. The Adolescent and Young Adult Cancer Global Accord team involves 26 investigators from Australia, New Zealand, the United States, Canada and the United Kingdom. Twenty-four consumers, including adolescents and young adults with cancer history and carers, informed study design. We describe methodology for a modified Delphi questionnaire. The questionnaire aims to determine optimal timing for end-of-life communication with adolescents and young adults, practice-related content needed in clinician training for end-of-life communication with adolescents and young adults, and desireability of evidence-based training models. Round 1 involves an expert panel of investigators identifying appropriate questionnaire items. Rounds 2 and 3 involve questionnaires of international multidisciplinary health-professionals, followed by further input by adolescents and young adults. A second stage of research will design health-professional training to support best-practice end-of-life communication. The outcomes of this iterative and participatory research will directly inform the implementation of best-practice end-of-life communication across Adolescent and Young Adult Cancer Global Accord countries. Barriers and training preferences identified will directly contribute to developing clinician-training resources. Our results will provide a framework to support further investigating end-of-life communication with adolescents and young adults across diverse countries. Our experiences also highlight effective methodology in undertaking highly collaborative global research.
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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 | 0.134 | 0.073 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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".