Fear of Cancer Recurrence and Fear of Cancer Progression, Digital Resource Engagement and Health Literacy: A Review
Bibliographic record
Abstract
Cancer care is evolving, and digital resources are being introduced to support cancer patients throughout the cancer journey. Logistical concerns, such as health literacy and the emotional experience of cancer, need to be considered. Fear of cancer recurrence (FCR) and fear of cancer progression (FOP) are relevant emotional constructs that should be investigated. This scoping review explored two main objectives: first, the link between FCR/FOP and engagement with digital resources, and second, the link between FCR/FOP and health literacy. A database search was conducted separately for each objective. Relevant papers were identified, data were extracted, and a quality assessment was conducted. Objective 1 identified two relevant papers that suggested that higher levels of FCR were correlated with lower levels of engagement with digital resources. Objective 2 identified eight relevant papers that indicated that higher FCR/FOP is correlated with lower health literacy. However, one paper with a greater sample size and a more representative sample reported no significant relationship. There may be important relationships between the constructs of FCR/FOP, resource engagement, and health literacy and relationships may differ across cancer type and sex. However, research is limited. No studies examined the relationship between FOP and engagement or FCR/FOP and digital health literacy, and the number of studies identified was too limited to come to a firm conclusion. Further research is needed to understand the significance and relevance of these relationships.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".