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Record W4411238797 · doi:10.1037/hea0001528

The interconnectedness of fear of cancer recurrence components: A network approach.

2025· article· en· W4411238797 on OpenAlexaff
Melanie P. J. Schellekens, Yvonne L Luigjes-Huizer, Allan Ben Smith, José A. E. Custers, Sébastien Simard, Sophie Lebel, Marije L. van der Lee

Bibliographic record

VenueHealth Psychology · 2025
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsUniversity of OttawaUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsCancerPsychologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: While nearly 60% of cancer survivors report a heightened fear of cancer recurrence (FCR), not all of them experience functional impairment and want professional psychological care. We applied the network approach to study how different components of FCR (symptoms, triggers, perceived risk, and coping strategies) are interconnected to both FCR severity and functional impairment to better understand which survivors are likely to require psychological care. METHOD: We applied network analysis to cross-sectional data from 3,370 cancer survivors from nine different countries, spanning Asia, Australia, Europe, and North America, from the international Fear of Cancer Recurrence Inventory database. The shortest path analysis was applied to study what components were directly connected to both FCR severity and functional impairment. RESULTS: FCR severity was mainly connected to symptoms and triggers while functional impairment was mainly connected to coping strategies. The shortest paths indicated that worry and bodily triggers were directly connected to both higher FCR severity and more functional impairment. CONCLUSION: Worry and bodily triggers appear to be core components of FCR that are experienced as impairing in daily life. Our findings suggest that assessing functional impairment, worry, and bodily triggers, in addition to FCR severity, could be valuable when screening for clinical levels of FCR. To further improve our conceptual understanding of FCR, future studies should apply intensive longitudinal designs to explore how these components interact over time and within the individual. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.403
Threshold uncertainty score0.437

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.141
GPT teacher head0.540
Teacher spread0.399 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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