“You Helped Create This, Help Me Now”: A Qualitative Analysis of Patients’ Concerns about Breast Implants and a Proposed Strategy for Moving Forward
Bibliographic record
Abstract
BACKGROUND: Some women with breast implants express concern about the safety of implants, fearing the possibility of breast implant-associated anaplastic large cell lymphoma (BIA-ALCL) and breast implant-related illness. METHODS: A qualitative analysis was performed to examine the perceived challenges, barriers, and worries experienced by these women. Convenience sampling was used to elicit responses from members of Canadian BIA-ALCL Facebook advocacy groups. Three independent coders read and reread the transcripts, using thematic analysis to identify emerging themes. RESULTS: Sixty-four women answered questions posed by the president of the Canadian Society of Plastic Surgeons regarding concerns about their breast implants. Five themes were identified: informing, listening, acknowledging, clarifying, and moving forward. Patients desire improved communication about possible risks before implantation and as new information becomes available (informing), sincere listening to their concerns (listening), acknowledgement that these disease entities may be real and have psychosocial/physical impact on patients (acknowledging), clarification of implant-related problems and their treatment (clarifying), and improved processes for monitoring and treatment of patients with identified problems (moving forward). Consideration of these themes in the context of the five domains of trust theory (i.e., fidelity, competence, honesty, confidentiality, and global trust) suggests significant breakdown in the doctor-patient relationship for a subset of concerned women. CONCLUSIONS: Concerns related to BIA-ALCL and breast implant-related illnesses have undermined some women's trust in plastic surgeons. Consideration of these five themes and their impact on the five domains of trust can guide strategies for reestablishing patients' trust in the plastic surgery community.
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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 | 0.028 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".