Trust in Physician in Relation to Blame, Regret, and Depressive Symptoms Among Women with a Breast Cancer Experience
Bibliographic record
Abstract
Following a diagnosis of breast cancer women experience considerable distress and often present with elevated symptoms of depression. A woman's relationship with her oncologist, and particularly trust in the physician, might influence depressive symptoms, as well as emotional and cognitive reactions to medical decisions made concerning treatment. To assess these relationships, women currently undergoing treatment for breast cancer (n = 40) and women who had previously been treated for breast cancer (n = 74) were asked about (1) trust in their physician, (2) who they blamed for negative events during treatment, (3) who made the treatment decisions, (4) regret, and (5) depressive symptoms. As well, community participants (n = 146) without breast cancer were asked about trust in their physician, levels of depression, and questions regarding blame if they hypothetically had breast cancer. Depression was greatest among women in treatment, and trust in physician was greatest among women posttreatment. However, trust in physician was neither related to depressive symptoms, decision making, nor responsibility for presence of metastases/relapse. Paradoxically, greater trust in physician was related to increased blame of the doctor for other negative events that had occurred. Furthermore, depressive scores were higher among women who blamed their doctor for negative events in comparison to women who ascribed blame to no one. As well, individuals who blamed themselves for negative events reported greater regret than individuals who blamed no one. Thus, though a woman may not hold her physician directly responsible for health outcomes, this relationship may be important to consider in other aspects of her psychological well-being.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".