A Systematic Review of the Reporting Quality of Qualitative Research in Breast Plastic Surgery
Bibliographic record
Abstract
Background: Qualitative research incorporates patients’ voices into scientific literature. To date, there has been no formal review of qualitative research in plastic surgery. The primary objective of this study was to evaluate the reporting quality of “breast specific” plastic surgery qualitative research. Secondary objectives were to record study methodology and examine associations between reporting quality and publication/journal characteristics. Methods: MEDLINE, Embase, Psychinfo, and PubMed were searched to identify qualitative studies in breast plastic surgery. Findings were presented with descriptive analysis. Reporting quality was evaluated using the Standards for Reporting Qualitative Research (SRQR), a 21-item checklist. Results: Eighty studies were included. The median SRQR score was 17/21 (range: 6-21). The lowest reported SRQR items were qualitative approach (n = 29/80, 36%) and data collection method (n = 36/80, 45%). Nine (11%) studies described following a reporting guideline. Articles published in nursing journals had the highest average SRQR scores (18.4/21). There was no significant difference between studies published before or after the publication of SRQR ( P = .06). Eighty-six percent of studies focused on patient experiences with breast reconstruction (n = 69/80). Conclusions: The introduction of the SRQR has not led to significant improvement in the reporting of qualitative research. Rationale for methodology was frequently missing. We recommend that investigators conducting qualitative research in breast plastic surgery ensure they provide a rationale for their methodology and become familiar with the SRQR reporting guideline.
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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 | 0.481 | 0.754 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.028 | 0.026 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".