Cancer Survivorship: Research Priorities at the National and International Levels
Bibliographic record
Abstract
With an increasing number of people living with and beyond a cancer diagnosis, research addressing the needs of this population has consistently been identified as one of the key priorities for a global survivorship agenda. Within an international context, US, UK and Canada have been key players in priority setting activities, with the consistency across these nations lending support for a global survivorship research agenda. Priorities identified include: development of tools and instruments for use in survivorship research; development of effective care models and interventions; investigation of long-term effects of cancer diagnosis and treatment on patients, their families and caregivers; and needs and characteristics of unique or disadvantaged populations. An overview of the research being undertaken in Australia suggests a high level of congruency with international priorities, with a wide spectrum of research addressing issues across the whole survivorship continuum. However, support is needed for further work to progress our understanding of survivorship issues within an Australian context, particularly in the areas of unique populations, lifestyle factors and effective care models.
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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 | 0.098 | 0.084 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.010 | 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".