The experience of fear of recurrence in Mexican breast cancer survivors: a qualitative study
Bibliographic record
Abstract
Abstract Background: In Mexico, fear of recurrence (FCR) is one of the most frequently reported psychological problems of breast cancer (BC) survivors. However, a detailed investigation of this unique cultural and developing context has yet needed to be led. This study uses a deductive qualitative approach to describe and analyze the FCR experiences of Mexican BC survivors based on the blended theoretical model of FCR. Methods: Participants were Mexican BC survivors older than 18 years, with a previous cancer diagnosis stages I‒III, who had completed their primary treatment and reported experiencing FCR. In-depth interviews were conducted and were analyzed using deductive thematic analysis. Results: Ten women were interviewed. After analysis, 4 categories emerged: 1) topography of FCR, 2) triggers of FCR, 3) specific fears, and 4) coping with FCR. The interviews revealed that women were worried about cancer recurrence and its consequences. They experienced deterioration in their quality of life in important domains, such as family life. Conclusions: The findings suggest that FCR is a complex phenomenon, where the characteristics of the disease are influenced by the cultural context (familism, traditions, and customs). This study provides a first look into the experience of suffering from FCR in the Mexican population, affirming the presence of concepts such as triggers, specific fears, and coping as constant factors of the FCR experience.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".