Biopsychosocial Factors Associated with Supportive Care Needs in Canadian Adolescent and Young Adult Cancer Survivors
Bibliographic record
Abstract
Adolescents and young adults (AYAs) represent an overlooked population in cancer survivorship care. Identifying the needs of AYAs can guide the development of tailored programs for this population. We conducted a cross-sectional descriptive analysis to identify biopsychosocial factors associated with AYA post-treatment supportive care needs and unmet needs using data obtained from the Experiences of Cancer Patients in Transitions Study of the Canadian Partnership Against Cancer, in collaboration with cancer agencies in the 10 Canadian provinces. The analysis focused on data from n = 530 AYAs between the ages of 18 and 34 who had undergone treatment within the past 5 years. Respondents reported a median of two moderate to big (MTB) physical needs (out of 9) and one unmet physical need, two MTB emotional needs (out of 6) with two unmet MTB emotional needs, and one (out of 5) practical need reported and one unmet MTB practical need. We found some common associations across supportive care domains. Income (lower) and more complex treatment were associated with high needs and unmet needs across the three domains. Respondents with a family doctor who was “very involved” in their cancer care had a lower number of unmet physical and emotional needs. Identifying those at risk of supportive care needs and developing tailored pathways in which they are proactively connected with tailored and appropriate resources and programs may help to reduce the number of unmet needs and improve cancer survivors’ quality of life.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".