Fear of cancer recurrence: testing a cognitive formulation across time in women with ovarian cancer
Bibliographic record
Abstract
Background: Lee-Jones and colleagues (1997) have proposed a comprehensive cognitive model of fear of cancer recurrence (FCR), however little research has utilized or fully tested this conceptual model. Additionally, the cross-sectional nature of most studies limits our understanding of the trajectory of FCR over time, and longitudinal research is greatly needed. Method: Patients completed assessment measures at baseline (Time 1) and three months post-baseline (Time 2). The three aims of this study were to (1) test the cognitive model of FCR within an ovarian cancer population; (2) examine model stability; and (3) test the predictive validity of the model. Results: An exploratory factor analysis (EFA) suggested a more parsimonious four-factor model relative to Lee-Jones et al.’s suggested model. Using the results of the EFA, structural equation modeling (SEM) was used to analyze the data-driven model, with findings revealing excellent model fit at Time 1, 2 (60, N=283) = 130.48, p< .001, 2 /df = 1.84, CFI = 0.95, RMSEA = .06, SRMR = .06. This same model was examined at Time 2, with findings revealing acceptable model fit; 2 (60, N=201) = 121.15, p < .001, 2 /df = 2.02, CFI =0.93, RMSEA = .07, SRMR = .07, thus confirming that configural invariance was met. Tests of predictive validity indicated that using the components of FCR at Time 1 to predict consequences at Time 2 resulted in adequate model fit, 2 (84, N=283) = 167.17, p < .001, CFI =0.94, RMSEA = .06, SRMR = .07, 2 /df = 1.99; however, the regression paths from the emotional experience and cognitive appraisals were not significant predictors of behavioural responses at Time 2. Discussion: Findings demonstrated that the emotional experience of FCR may be far more complex for ovarian cancer patients than previously suggested which has important treatment implications. The current study is the first to evaluate the relative stability of the components of a data-driven model of FCR, with results revealing that the majority of ovarian cancer patients experience FCR, which is stable across a three-month period. Findings suggest that screening for FCR would be beneficial across the cancer experience.
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 | 0.007 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".