Examining Predictors of Fear of Cancer Recurrence Using Leventhal’s Commonsense Model
Bibliographic record
Abstract
BACKGROUND: Fear of cancer recurrence (FCR) is a common concern for survivors. Oncology nurses have a unique opportunity to identify survivors at increased risk of heightened FCR. Understanding predictors of FCR would be useful for this purpose; however, results about FCR predictors are inconsistent. OBJECTIVE: To examine empirically inconsistent predictors of FCR as guided by Leventhal's Commonsense Model. METHODS: A cross-sectional survey design was used to assess FCR, sociodemographic and clinical characteristics, and characteristics of the self (self-esteem and generalized expectancies) among cancer survivors. Structural equation modeling was used to examine predictors of FCR. RESULTS: Among 1001 participants, the mean time since diagnosis was 9.07 years, and most were diagnosed with breast cancer (65.93%). The strongest predictor of higher FCR was belief that knowing someone with a recurrence affects one's own level of FCR, although knowing someone with a recurrence actually predicted lower FCR. Other significant predictors of higher FCR were having 1 or more symptoms attributed to cancer, lower self-esteem, younger age, female gender, lower pessimism, longer time since diagnosis, and active follow-up at the survivorship clinic. CONCLUSION: Cancer survivors' perceptions are among an important series of variables that may predict higher levels of FCR. Oncology nurses are uniquely situated to identify the subset of cancer survivors with levels of FCR requiring professional intervention. IMPLICATIONS FOR PRACTICE: Oncology nurses can use the predictors indicated in this study to identify survivors with greatest need for coping with FCR to facilitate expedient intervention and/or referral to psychosocial providers.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".