Perspectives of Canadian adolescent and young adult (AYA) cancer survivors in designing a regional AYA survivorship program
Bibliographic record
Abstract
PURPOSE: Adolescents and young adults (AYA, ages 15-39 years) with cancer have distinct survivorship needs due to the unique physical, psychological, and social disruptions at this age. We sought to identify key program components of an AYA survivorship program and how to best implement these requirements. METHODS: An anonymous electronic survey was sent through the institutional electronic health record to AYA cancer survivors who completed treatment between 2015 and 2020 at the Ottawa Hospital (Ottawa, Canada). Demographic data were analyzed using descriptive statistics. Quantitative data from Likert scales were presented as frequency distributions of responses. Ranking items were sorted according to weighted mean. Qualitative data from open-ended responses were analyzed using content analysis. RESULTS: In total, 85 respondents (response rate 24.9%) completed the survey. Respondents were predominantly female (79.5%), diagnosed in 2020 (25%) or 2019 (20.8%), employed full-time (61.1%), married (62.5%), and had children at diagnosis (52.8%). The top three priorities were (1) preventing new recurrences, (2) reducing anxiety and improving mental health, and (3) feeling "normal." Most respondents (78.3%) indicated that they would engage with a formal program, with a preference for electronic communication tools (86.2%). Respondents preferred a dedicated oncology provider (64.7%). Thematic analysis suggested that a focus on mental health would enhance support for survivors and increase utilization of the program. CONCLUSIONS: AYA survivorship programming in our region requires the integration of mental health supports as a key resource component to address patient-identified needs and increase program utilization. IMPLICATIONS FOR CANCER SURVIVORS: These findings highlight the need for survivorship programs to integrate robust mental health supports. This will increase program utilization and facilitate access to screening and late effects monitoring.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| 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".