Barriers and Enablers to Engaging with Long-Term Follow-Up Care Among Canadian Survivors of Pediatric Cancer: A COM-B Analysis
Bibliographic record
Abstract
Survivors of pediatric cancer are at risk for late effects and require risk-adapted long-term follow-up (LTFU) care. Yet less than 50% of survivors attend LTFU care. This study aimed to identify barriers and enablers of engaging with LTFU care as perceived by Canadian survivors of pediatric cancer and healthcare providers (HCPs). Survivors (n = 108) and HCPs (n = 20) completed surveys assessing barriers and enablers to attending LTFU care, summarized using descriptive statistics. Participants were invited to participate in survivor focus groups (n = 22) or HCP semi-structured interviews (n = 7). These were analyzed using reflexive thematic analysis and the Capability, Opportunity, and Motivation for Behaviour Change (COM-B) model, which explores how an individual’s capability, opportunity, and motivation influence a target behaviour. Structural barriers, transitioning from pediatric to adult care, and time constraints were highlighted as barriers that affect survivors’ physical opportunity to engage in LTFU care. Accessibility, financial support, HCPs and family support, and community resources were highlighted as enablers that better survivors’ physical and social opportunity to engage in LTFU care. In conclusion, Canadian survivors of pediatric cancer highlighted barriers that limited their physical opportunity to attend LTFU care, while factors that enhanced their physical and social opportunities facilitated greater engagement with LTFU care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".