Remote Psychological Interventions for Fear of Cancer Recurrence: Scoping Review
Bibliographic record
Abstract
BACKGROUND: Patients with cancer and survivors may experience the fear of cancer recurrence (FCR), a preoccupation with the progression or recurrence of cancer. During the spread of COVID-19 in 2019, patients and survivors experienced increased levels of FCR. Hence, there is a greater need to identify effective evidence-based treatments to help people cope with FCR. Remotely delivered interventions might provide a valuable means to address FCR in patients with cancer. OBJECTIVE: The aim of this study is to first discuss the available psychological interventions for FCR based on traditional cognitive behavioral therapies (CBTs) or contemporary CBTs, in particular, mindfulness and acceptance and commitment therapy, and then propose a possible approach based on the retrieved literature. METHODS: We searched key electronic databases to identify studies that evaluated the effect of psychological interventions such as CBT on FCR among patients with cancer and survivors. RESULTS: Current evidence suggests that face-to-face psychological interventions for FCR are feasible, acceptable, and efficacious for managing FCR. However, there are no specific data on the interventions that are most effective when delivered remotely. CONCLUSIONS: CBT interventions can be efficacious in managing FCR, especially at posttreatment, regardless of whether it is delivered face to face, on the web, or using a blended approach. To date, no study has simultaneously compared the effectiveness of face-to-face, web-based, and blended interventions. On the basis of the retrieved evidence, we propose the hypothetical program of an intervention for FCR based on both traditional CBT and contemporary CBT, named Change Of Recurrence, which aims to improve the management of FCR in patients with cancer and survivors.
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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 | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".