Exploring Women’s Support Needs After Breast Reconstruction Surgery
Bibliographic record
Abstract
BACKGROUND: The procedures for breast reconstruction (BR) after mastectomy frequently initiate a difficult recovery period. A better understanding of women's support needs after surgery would improve patient care. OBJECTIVE: The aim of this study was to identify patients' support needs after BR. METHODS: In a retrospective study design, 21 participants described their support experiences after BR, including their sources of support and the impact of support on their recovery in a semistructured interview. Transcriptions of the interviews were analyzed using thematic analysis. RESULTS: Four support needs were identified and were composed of elements of instrumental, emotional, and informational support. These needs were addressed to varying degrees by healthcare providers, family members, and other women who had BR experience. CONCLUSION: Women's experience of BR and their ability to cope are markedly better when their support needs are effectively addressed. Greater attention to their needs for support has the potential to improve patient care. IMPLICATIONS FOR PRACTICE: Nurses play a pivotal role in providing information to women who are recovering from BR. Improved access to communication channels between nurses and patients would likely improve patients' support experiences. In addition, nurses can assess the women's specific support needs and partner with families to help them understand how best to support women during recovery.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".