Setting Priorities for a Provincial Adolescent and Young Adult Oncology Program
Bibliographic record
Abstract
Adolescent and young adult (AYA, ages 15-39 years) oncology patients are an underserved population with specialized needs. AYA programs are absent from most Canadian centers. We identified a priority list and sequence for new programs to address. Program goals, priorities, and activities were developed through literature review, national consensus documents, and expert opinion. Health care providers (HCPs) involved in AYA cancer care, administrators, and patient and family representatives were engaged to co-develop program goals and activities. A modified Delphi technique was used through two iterations followed by an in-person meeting to prioritize program implementation. Consensus was defined as a mean score of less than 2.0 (not important) or 4.0 or greater (important). Items without consensus (scored between 2.0 and 3.99) were discussed at the in-person meeting. Sixty provincial stakeholders completed the Delphi survey across multiple disciplines. Twenty-seven stakeholders attended the in-person meeting. All goals were deemed important, except development of a research program. Patient implementation tasks ranked highest. Priority sequence of implementation was: patient care first, followed by HCP education; patient and family education; program sustainability plan; evaluation; research; then a model for multidisciplinary tumor board review. These represent key goals for new AYA oncology programs and a priority sequence of implementation.
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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 | 0.021 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".