Discharge needs among women with mastectomy: a suggested nursing care plan
Bibliographic record
Abstract
Background Breast cancer is the most commonly diagnosed cancer for women worldwide. Mastectomy is widely used in the surgical management of breast cancer. It affects women emotionally, psychologically, spiritually, and physically. Identification of the patients needs after mastectomy can help them cope with their condition as well as help nurses develop a guided discharge care plan. Aim To determine discharge needs among women with mastectomy and establish suggested nursing care plans. Design A descriptive exploratory research design was used in the study. Setting The study was conducted at the surgical department in the breast cancer hospital at Eltagamoah El-awal City subordinate to the National Cancer Institute affiliated with Cairo University, Egypt. Sample A purposive sample of 100 adult women with breast cancer who performed a Modified Radical Mastectomy. Tools Two tools were utilized to collect data: (1) personal and medical background data form. (2) adapted Toronto Informational Needs Questionnaire of Breast Cancer. Results The highest informational needs among studied women with mastectomy prior discharge were spiritual needs with a mean percentage (88.8%), followed by physical needs (87.8%), then treatment and complications with a mean percentage (of 87.2%). Conclusion Based on the results of the study, the studied women with mastectomy before discharge had informational needs in this order; spiritual, physical, treatment and complications, disease, and informational needs about investigation and tests, while the least dimension was psychosocial needs. Recommendation A longitudinal study is recommended to determine the long-term effect of unmet needs especially before hospital discharge after mastectomy.
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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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".