Beauty Therapy to Support Psychosocial Recovery from Oncological Care: A Qualitative Research on the Lived Experience of Women with Breast Cancer Treated with Chemotherapy
Bibliographic record
Abstract
During the oncological care path, breast cancer patients treated with chemotherapy suffer from a number of psycho-physical changes, and appearance-related side effects are among the primary determinants of psychosocial impairment. Appropriate interventions are needed due to the fact that treatment-induced transformations have been associated with a decline in overall quality of life, interpersonal and sexual difficulties, and adverse effects on therapeutic adherence. In the framework of integrative oncology, beauty therapy is an affordable and straightforward intervention that could be used in the clinical management of breast cancer side effects. This study aims to comprehend the emotional and lived experiences of women undergoing chemotherapy after a brief beauty therapy intervention with licensed beauticians. The Interpretative Phenomenological Analysis was used as a methodological guideline. Sixteen women were purposefully recruited in a day hospital of a cancer unit, where the beauty therapy was implemented. At the end of the intervention, data were gathered using a semi-structured interview with open-ended questions. A thematic analysis was performed on verbatim transcriptions. Findings support the proposal of beauty therapy for patients undergoing chemotherapy. Assuming a relational viewpoint, beauty therapy could improve patients' feelings about themselves and the way they feel about others, even if they do not declare a specific interest in their outward appearance.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".