The Experience of Patients with Endocrine Therapy for Breast Cancer: A Patient Journey Map Based on Qualitative Research
Bibliographic record
Abstract
(1) Background: While there is extensive documentation on the medical experience of breast cancer, a thorough understanding of the various stages of endocrine therapy remains insufficient. The aim of this study was to map the experiences and coping styles of breast cancer patients during endocrine therapy. (2) Methods: Qualitative research was conducted to gather insights into the experiences of breast cancer patients undergoing endocrine therapy. The themes were organized through content analysis and induction. Subsequently, patients were invited for face-to-face interviews at a top-three hospital in Guangzhou to supplement and validate the findings from the literature review. The patient journey was then mapped based on both the literature review and the semi-structured interviews. (3) Results: A total of 24 studies were included that described patients' experiences and behaviors during the early, middle, and late stages of treatment, leading to the formation of a preliminary framework. Interviews were conducted with 20 patients, which confirmed and enriched the findings from the literature review. Based on these results, a stage trajectory for endocrine therapy in breast cancer was established. (4) Conclusions: The patient journey map developed in this study clearly and intuitively illustrates the thought and emotion matrix, as well as the behavior matrix, of breast cancer patients undergoing endocrine therapy. This provides a theoretical foundation for enhancing clinical services tailored to the needs of these patients.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".