Illness Perceptions, Fear of Cancer Recurrence, and Mental Health in Teenage and Young Adult Cancer Survivors
Bibliographic record
Abstract
Background: The Common-Sense Model of illness self-regulation underpins illness-specific cognitions (including both illness perceptions and a fear of cancer recurrence; FCR). There is evidence in adults of associations between FCR, illness perceptions, and mental health in adult cancer survivors. However, there is limited empirical research examining these constructs within the developmentally distinct population of adolescent and young adult (AYA) survivors of cancer. The current study aimed to bridge that gap to inform potentially modifiable treatment targets in this population. Method: A cross-sectional, correlational design was used to examine the associations between illness perceptions, FCR, and mental health. A web-based survey was completed by a convenience sample of AYA survivors. Regression and mediation analyses were performed. Results: Overall, more negative illness perceptions were associated with more severe FCR and greater depressive and anxiety symptomatology. Higher FCR was predictive of worse overall mental health. More negative overall illness perceptions predicted the relationship between FCR–depression, mediating 24.1% of the variance. Contrastingly, overall illness perceptions did not predict or mediate the relationship between FCR–anxiety. However, the specific illness perceptions regarding timeline, personal control, and emotional representation, were predictive of the FCR–anxiety relationship. Discussion: Illness perceptions and FCR were predictive of mental health outcomes. Identifying and therapeutically targeting negative illness perceptions in those young adults who have survived adolescent cancer could therefore be a means of reducing anxiety and depressive symptomatology. Limitations and future directions are discussed.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".