Patient Information Needs and Breast Reconstruction After Mastectomy
Bibliographic record
Abstract
BACKGROUND: Although many women benefit from breast reconstruction after mastectomy, several studies report women's dissatisfaction with the level of information they were provided with before reconstruction. OBJECTIVE: The present meta-synthesis examines the qualitative literature that explores women's experiences of breast reconstruction after mastectomy and highlights women's healthcare information needs. METHODS: After a comprehensive search of 6 electronic databases (CINAHL, Cochrane Library, EMBASE, MEDLINE, PsycINFO, and Scopus), we followed the methodology for synthesizing qualitative research. The search produced 423 studies, which were assessed against 5 inclusion criteria. A meta-synthesis methodology was used to analyze the data through taxonomic classification and constant targeted comparison. RESULTS: Some 17 studies met the inclusion criteria, and findings from 16 studies were synthesized. The role of the healthcare practitioner is noted as a major influence on women's expectations, and in some instances, women did not feel adequately informed about the outcomes of surgery and the recovery process. In general, women's desire for normality and effective emotional coping shapes their information needs. CONCLUSION: The information needs of women are better understood after considering women's actual experiences with breast reconstruction. It is important to inform women of the immediate outcomes of reconstruction surgery and the recovery process. IMPLICATIONS FOR PRACTICE: In an attempt to better address women's information needs, healthcare practitioners should discover women's initial expectations of reconstruction as a starting point in the consultation. In addition, the research revealed the importance of the nurse navigator in terms of assisting women through the recovery process.
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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 | 0.014 | 0.096 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".