Evaluating Fatalism Among Breast Cancer Survivors in a Heterogeneous Hispanic Population: A Cross-Sectional Study
Bibliographic record
Abstract
Hispanic breast cancer survivors reported worse quality of life, and fatalism is considered one of the mediators for this disparity. This study aimed to identify the factors associated with fatalism within a diverse Hispanic population. Hispanic origin was self-reported, and the Multidimensional Fatalism Measure questionnaire, a validated tool that measures fatalism across multiple dimensions, was used to assess fatalism. A total of 390 women, consisting of 210 Puerto Ricans, 34 Colombians, 29 Dominicans, 25 Cubans, 24 Venezuelans, 22 Mexicans, and 46 individuals of other Hispanic backgrounds, completed the fatalism assessment. The mean fatalism score was 16.4 (95% CI = 15.8-17.0), characterized by a high internal locus of control and strong religious beliefs, along with moderate beliefs in luck and a low external locus of control. The higher fatalism scores were reported in Dominican, Mexican, and Venezuelan groups, while Colombians reported the lowest score. Multivariable analysis showed that Colombians (β = -4.0), individuals with higher household incomes (β = -2.3 for USD 20,000-USD 75,000, β = -2.4 for ≥75,000), higher education levels (β = -1.9), and those using English more frequently at home (β = -2.0) reported lower fatalism compared to their reference group. To enhance the quality of life for these survivors, culturally tailored interventions should focus on improving perceived control and mitigating fatalism.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".