Chemotherapy-induced nausea and vomiting: exploring patients’ subjective experience
Bibliographic record
Abstract
BACKGROUND: This study aimed to explore the subjective experience of nausea and vomiting during chemotherapy treatment among breast cancer patients and the impacts on their daily lives. METHODS: A qualitative descriptive study was conducted in breast cancer patients who received chemotherapy and had experienced nausea and/or vomiting. Semi-structured interviews were conducted and analyzed using content analysis based on Giorgi's method. RESULTS: Of 15 patients who participated, 13 were included in the final analysis (median age =46 years, interquartile range [IQR] =6.0; all were Malays). Vomiting was readily expressed as the "act of throwing up", but nausea was a symptom that was difficult to describe. Further exploration found great individual variation in patterns, intensity, and impact of these chemotherapy-induced nausea and vomiting (CINV) symptoms. While not all patients expressed CINV as bothersome, most patients described the symptom as quite distressing. CINV was reported to affect many aspects of patients' lives particularly eating, physical, emotional, and social functioning, but the degree of impacts was unique to each patient. One of the important themes that emerged was the increase in worship practices and "faith in God" among Malay Muslim patients when dealing with these adverse effects. CONCLUSION: CINV continues to be a problem that adversely affects the daily lives of patients, hence requiring better understandings from the health care professionals on patients' needs and concerns when experiencing this symptom.
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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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".