Effect of a novel pilot support group on distress and quality of life in breast cancer patients in Nigeria
Bibliographic record
Abstract
OBJECTIVE: To assess the effect of a new breast cancer support group (BCSG) on breast cancer patients' self-reported distress and quality of life. METHODS: A single arm pre-post trial providing an eight session healthcare provider led BCSG. Primary outcome variables were distress and quality of life assessed using the National Comprehensive Cancer Network Distress Thermometer (DT) and The Functional Assessment of Cancer Therapy - Breast plus Arm Morbidity (FACT-B+4), respectively. Topics in each session addressed a wide range of issues some of which were pre-selected by the patients themselves. DATA ANALYSIS: Paired sample t-test was used for data analysis on International Business Machine Statistical Package for the Social Sciences 21. RESULTS: The participants (N = 18) had a mean age of 51. Most had secondary school level education (54%), were traders (59%) and had stage 3 or 4 disease (67%). A larger proportion (78% or n = 14) of the patients lived in rural areas, while 4 (22%) of the patients lived in Ibadan. Out of the 8 sessions, 12 (67%) of the participants attended 1-3 sessions while 6 (33%) attended 4-8 sessions. There were, significant improvements in emotional wellbeing (t = -4.253; p < 0.05) and functional wellbeing (t = -2.191; p < 0.05) on the FACT-B+4. There was a significant reduction in the DT score (t = 2.345; p < 0.05) but the number of items on the problem list were not significantly reduced (t = 1.191; p > 0.05). Majority (75%) of the patients rated the support group activities as satisfactory. CONCLUSION: These data show that the support group can benefit breast cancer patients in terms of reduced distress levels along with satisfaction and improvement in the functional and emotional wellbeing indices of quality of life.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".