Adoption of Surgical Innovations
Bibliographic record
Abstract
PURPOSE: Sentinel lymph node biopsy (SLNB) has been unevenly adopted into practice in Canada. In this qualitative study, the authors explored individual, institutional, and policy factors that may have influenced SLNB adoption. This information will guide interventions to improve SLNB implementation. METHODS: Qualitative methodology was used to examine factors influencing SLNB adoption. Grounded theory guided data collection and analysis. Semistructured interviews were based on Roger's diffusion of innovation theory. Purposive and snowball sampling was used to identify participants. Semistructured telephone interviews were conducted with urban, rural, academic, and community health care providers and administrators to ensure all perspectives and motivations were explored. Two individuals independently analyzed data and achieved consensus on emerging themes and their relationship. RESULTS: A total of 43 interviews were completed with 21 surgeons, 5 pathologists, 7 nuclear medicine physicians, and 10 administrators. Generated themes included awareness of SLNB with the exception of some administrators, acknowledged advantage of SLNB, SLNB compatibility with beliefs regarding axillary staging, acknowledgment that SLNB was a complex innovation to adopt, extensive trialing of SLNB prior to adoption, observable benefits with SLNB, acknowledgment that hospital-level administrative support enabled adoption, desire for a provincial policy supporting SLNB to assist in hospital-level adoption, requirement of a local high-volume breast surgery champion who communicated extensively with team to facilitate local adoption, and need for credentialing of SLNB to ensure quality. CONCLUSIONS: SLNB is a complex innovation to adopt. Successful adoption was assisted by a high-volume breast cancer surgical champion, interprofessional communication, and administrative support.
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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 | 0.016 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".