Guidelines for Caring for the Social Well-Being of Adolescents and Young Adults with Cancer in Australia
Bibliographic record
Abstract
More than 1000 Australian adolescents and young adults (AYAs) are diagnosed with cancer annually. Many report unmet social well-being needs, which impact their mental health. Australian AYA cancer care providers lack guidance to address these needs well. We aimed to develop guidelines for caring for the social well-being of AYAs with cancer in Australia. Following the Australian National Health and Medical Research Council guidance, we formed a multidisciplinary working group ( n = 4 psychosocial researchers, n = 4 psychologists, n = 4 AYA cancer survivors, n = 2 oncologists, n = 2 nurses, and n = 2 social workers), defined the scope of the guidelines, gathered evidence via a systematic review, graded the evidence, and surveyed AYA cancer care providers about the feasibility and acceptability of the guidelines. The guidelines recommend which AYAs should have their social well-being assessed, who should lead that assessment, when assessment should occur with which tools/measures, and how clinicians can address AYAs' social well-being concerns. A key clinician, who is knowledgeable about AYAs' developmental needs, should lead the assessment of social well-being during and after cancer treatment. The AYA Psycho-Oncology Screening Tool is recommended to screen for social well-being needs. The HEADSSS Assessment (Home, Education/Employment, Eating/Exercise, Activities/Peer Relationships, Drug use, Sexuality, Suicidality/Depression, Safety/Spirituality Assessment) can be used for in-depth assessment of social well-being, while the Social Phobia Inventory can be used to assess social anxiety. AYA cancer care providers rated the guidelines as highly acceptable, but discussed many feasibility barriers. These guidelines provide an optimal care pathway for the social well-being of AYAs with cancer. Future research addressing implementation is critical to meet AYAs' social well-being needs.
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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 | 0.034 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".