The impact of occupation-based problem-solving strategies training in women with breast cancer
Bibliographic record
Abstract
BACKGROUND: By identifying the occupations of women with breast cancer who have performance problems, to examine the impact of the application of occupation-based problem-solving strategies (OB-PSS) training on cancer-related fatigue, depression, and quality of life. METHODS: The study comprises 22 women outpatients in the clinic. Socio- demographic and Clinical Features Information Collection Form, Canadian Occupational Performance Measure (COPM), Cancer Fatigue Scale (CFS), Beck Depression Inventory (BDI), The European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire C-30 and BR23 (EORTC QOL-C30 - EORT QOL-BR23) tests have been applied to survivors. OB-PSS training was conducted on a face-to-face basis once a week for 6 weeks. RESULTS: When activity distribution results in accordance with the performance areas are studied, women with breast cancer were seen to suffer problems mostly in their most productive areas (housework management). As a means of solving these performance problems, they developed adaptive strategies like including additional new steps to these activities. Statistically meaningful results have been obtained between measurements before and after the treatment process through all tests (p < 0.01). CONCLUSIONS: OB-PSS provides positive gains in women with breast cancer in terms of a reduction in the degree of cancer-related fatigue and depression, and a progress in performance and satisfaction levels particularly in activities where performance problems are experienced and an improvement in quality of life. OB-PSS training could be used as an appropriate rehabilitation approach for coping with problems in women' life with breast cancer.
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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 | 0.001 |
| 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) | 0.002 | 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".