Breast cancer patients’ experiences of patient–doctor communication: a working relationship
Bibliographic record
Abstract
The traumas of diagnosis and treatment for breast cancer are well researched and generally addressed in care. While women with breast cancer continue to identify the need for better communication with physicians, studies to date have not investigated how the process of communication between physicians and women with breast cancer actually unfolds. This phenomenological study therefore explored how women with breast cancer experience patient-physician communication to gain a greater understanding of effective approaches. Interviews of a purposeful sample of 11 women within 6 months of initial diagnosis or recurrence of breast cancer were audiotaped, transcribed verbatim and analyzed using inductive interpretation. Themes and patterns of positive and negative experiences emerged. All experiences began with the woman's feeling of vulnerability. In positive experiences, information sharing and relationship building were inextricably linked components of a working relationship which was at the same time affective, behavioural and instrumental. This experience, in turn, influenced the woman's experience of control and mastery of the illness experience, and their experience of learning to live with breast cancer. Findings illuminate the importance of comprehensively patient-centred, working relationships. Several specific techniques to enhance effective communication are identified.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".