Pilot study: Evaluation of the use of the convergent interview technique in understanding the perception of surgical design and simulation
Bibliographic record
Abstract
BACKGROUND: It is important to understand the perceived value of surgical design and simulation (SDS) amongst surgeons, as this will influence its implementation in clinical settings. The purpose of the present study was to examine the application of the convergent interview technique in the field of surgical design and simulation and evaluate whether the technique would uncover new perceptions of virtual surgical planning (VSP) and medical models not discovered by other qualitative case-based techniques. METHODS: Five surgeons were asked to participate in the study. Each participant was interviewed following the convergent interview technique. After each interview, the interviewer interpreted the information by seeking agreements and disagreements among the interviewees in order to understand the key concepts in the field of SDS. RESULTS: Fifteen important issues were extracted from the convergent interviews. CONCLUSION: In general, the convergent interview was an effective technique in collecting information about the perception of clinicians. The study identified three areas where the technique could be improved upon for future studies in the SDS field.
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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 | 0.056 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".