Coping Strategies and Perception of Patients With Breast Cancer Post‐Surgery in an Underserved Region of Nigeria
Bibliographic record
Abstract
BACKGROUND: Patients' coping styles, perceptions, and attitudes are important for healthy living and survivorship in breast cancer. AIM: To assess factors associated with coping styles in patients with breast cancer surgery in an underserved population in Northern Nigeria. METHODS: This cross-sectional study included 72 patients with breast cancer post-surgery. Data was collected on clinico-demographic variables, patients' perceptions, the World Health Organization Quality of Life (WHOQOL-BREF), the General Health Questionnaire (GHQ-12), and the Coping Strategy Inventory (CSI-32). RESULTS: The mean age of participants was 45.9 (±9.1) years. Over 80% of participants underwent mastectomy, with 93.1% reporting complications. Notwithstanding, participants largely perceived that the procedure was life-saving, and most did not feel the need for a breast prosthesis. Notably, 70.8% reported an increased tendency to withdraw socially, but only 34.7% experienced functional difficulties with daily activities or chores. Patients practiced different engagement-disengagement coping strategies, albeit use of multiple dimensions of engagement coping was pronounced in a greater fraction (percentile) of patients. Multiple aspects of perception (including a lack of satisfaction with clothes fitting, feelings of incompleteness, greater time since surgery), and the experience of psychological distress were associated with disengagement coping. On the other hand, engagement coping was more likely in patients who were satisfied with clothing fit, experienced less impact on daily living, and had an improved quality of life. CONCLUSION: Coping in patients with breast cancer post-surgery is multifaceted, varying by individual perception and psychosocial wellbeing. Future research is needed to guide interventions that bolster psychosocial well-being to promote healthy coping.
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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 | 0.001 | 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".