Predictors of symptom distress in women with breast cancer during the first chemotherapy cycle
Bibliographic record
Abstract
PURPOSE: To determine the extent to which personal characteristics and "person factors" predict symptom distress during the first cycle of chemotherapy. DESIGN: Prospective, longitudinal, correlational. SAMPLE AND SETTING: 120 women with Stage I and II breast cancer starting their first cycle of chemotherapy were recruited from six diverse oncology settings. METHODS: Self-report questionnaires were completed prior to the beginning, the nadir, and the end of the first chemotherapy cycles. MAIN RESEARCH VARIABLES: Personal characteristics, "person factors", and symptom distress. FINDINGS: Optimism and external locus of control predicted low symptom distress levels at the both the nadir and at the end of the first cycle. Fatigue, appearance, and insomnia caused the greatest distress with higher symptom distress scores reported at the nadir with a mean item score of 1.98 on a five-point Likert scale. CONCLUSIONS: Women who maintained a positive outlook, and trusted their health care providers experienced lower levels of symptom distress. Findings suggest that most women experienced some symptom distress, particularly during the middle of the first cycle of chemotherapy.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".