Measuring the Impact of an Adolescent and Young Adult Program on Addressing Patient Care Needs
Bibliographic record
Abstract
PURPOSE: We aimed to evaluate the effectiveness of an adult-based adolescent and young adult (AYA) cancer program by assessing patient satisfaction and whether programming offers added incremental benefit beyond primary oncology providers (POP) to address their needs. METHODS: A modified validated survey was used to ask two questions: (1) rate on a 10-point Likert scale their level of satisfaction with the information provided to them by their POP and (2) did the AYA consult provide added value on top of their POP. Young people at PM were recruited over two separate time points spaced 1 year apart. Descriptive statistics was used to report demographics and survey responses. Differences in demographics between cohorts 1 and 2 were compared using Student's t-tests. RESULTS: Participants were an average of 31 years (range 15-39) of age; (Cohort 1 = 137; Cohort 2 = 130) and were dominated by diagnoses of leukemia, lymphoma, and breast cancer. More patients had a consultation with the AYA program in 2016 (Cohort 2 = 55/130, 42%) compared to 2015 (Cohort 1 = 34/137, 25%, p = 0.026). Mean satisfaction scores (±SD) with information provided by POP in AYA domains in both cohorts combined were highest among (1) cancer information (8.09 ± 2.22), (2) social supports (7.45 ± 2.52), and (3) school/work (7.42 ± 2.88). When evaluating the incremental benefit of the AYA-dedicated team, statistically significant added value was perceived in 5/10 domains, including school/work (p < 0.001), social supports (p < 0.001), physical appearance (p = 0.009), sexual health (p = 0.01), and fertility (p < 0.001). CONCLUSIONS: Participants were satisfied with the information provided by their POP and still declared incremental added benefit of the AYA program. Cancer centers should continue to advocate for AYA focused programming with ongoing evaluation.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".