Commentary: Advancing Understanding of Sociodemographic Variables Impacting Transition in AYAs Diagnosed with Cancer
Bibliographic record
Abstract
There are approximately 400,000 survivors of pediatric cancer in the United States (Phillips et al., 2015). Given the potential late effects arising as a consequence of their disease and treatment, it is imperative that these survivors continue to receive follow-up care as adults (Landier et al., 2004; Michel et al., 2019). Despite the importance of long-term monitoring, less than one-third of survivors receive survivor-specific adult care (Nathan et al., 2008). Several reviews have highlighted transition barriers and facilitators across pediatric populations (e.g., Gray et al., 2018; Otth et al., 2021). However, further work remains needed regarding the role of socioecological factors impacting the transition of pediatric cancer survivors. Prussien and colleagues’ (this issue) study advances transition research by examining the influence of sociodemographic factors on transition readiness in adolescent and young adult (AYA) cancer survivors, aged 15–29 years. Specifically, they assessed individual and cumulative sociodemographic factors and used the Transition Readiness Inventory to assess beliefs, expectations, and goals related to the transition process. They also administered the Health Competence Beliefs Inventory assessing health perceptions, healthcare satisfaction, cognitive competence, and autonomy. Their aims were (a) to determine the relation among sociodemographic factors, cumulative effects, and transition beliefs/expectations and goals, and (ii) to examine the moderating role of health competence beliefs in AYA survivors of childhood cancer. Results indicate that insurance type was the only sociodemographic factor significantly associated with transition readiness and health competence beliefs. AYAs with public insurance reported lower cognitive competence, healthcare satisfaction, and transition goals. These results suggest that an important transition obstacle may be structural or institutional, rather than a lack of motivation in obtaining adult follow-up care (Prussien et al., this issue).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".