Attending Childhood Cancer Follow‐Up Care: Travel Time, Disparities and Health—A Canadian Cohort Study
Bibliographic record
Abstract
OBJECTIVE: Despite the importance of follow-up care for childhood cancer survivors (CCS), adherence remains below recommended levels. Potential barriers include geographical distance. We aimed to improve our understanding of the accessibility of survivorship follow-up care at a Long Term Survivor Clinic (LTSC). METHODS: Questionnaire data on health status of CCS enrolled in a LTSC in Calgary, Canada (collected between 2021 and 2024) was linked to CCS' medical records and the Canadian Index of Multiple Deprivation via postal codes. Linear, logistic, and negative binomial regression models were conducted to explore the association between health status and travel time, and between health status and socio-economic situation, and demographic and treatment-related factors. RESULTS: We included 203 CCS (48% female; mean age = 23 years; mean time after diagnosis = 14 years). Most CCS (75%) lived less than 1 hour away from the LTSC and traveled from within the province. Travel time was not significantly associated with health status. Health status was significantly associated with sex, time since diagnosis, and certain socioeconomic factors. Females reported more current health problems than males (IRR = 2.170; p < 0.001) and higher anxiety scores (β = 4.109; p = 0.011). Socio-economic factors were associated with reporting more depressive symptoms (β = 3.835; p = 0.040) and fear of second cancers (OR = 2.375; p = 0.022) and a more recent diagnosis with fear of cancer recurrence (OR = 0.873; p < 0.001). CONCLUSIONS: Instead of travel time, individual factors were associated with health status, providing opportunities for targeted interventions to ensure continued attendance. Enhancing general LTFU access through location-appropriate services and addressing underlying socio-economic inequalities are crucial to ensure engagement in LTFU care and improve health outcomes for CCS.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".