Partner Abandonment of Women with Breast Cancer: <i>Myth or Reality?</i>
Bibliographic record
Abstract
PURPOSE: The purpose of this article is to determine the existing evidence related to marital breakdown after a breast cancer diagnosis by reviewing studies that highlight two current belief models: the lay belief model and the clinical belief model. OVERVIEW: The small number of studies conducted on this topic since 1988 revealed no data to confirm the lay belief model, which proposes that women with breast cancer are abandoned by their partners. The evidence appears to support the clinical belief model that the majority of marital relationships remain stable after breast cancer and that breakdown is most likely in those relationships with pre-existing difficulties. CLINICAL IMPLICATIONS: This review indicates that it may be important for clinicians to routinely ask about the quality of the marital relationship as part of the initial assessment, because it appears that this may be a main predictor of post-diagnosis marital adjustment. In addition, greater dissemination of the findings of this review through the media and through cancer organizations is needed to more accurately reflect the experience of couples facing breast cancer and, thus, to begin to change the public perception of partner desertion after breast cancer. This could help both women with breast cancer and women from the general population who may one day confront a breast cancer diagnosis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".