Factors Influencing Plastic Surgeons When Selecting New Colleagues
Bibliographic record
Abstract
Introduction: As plastic surgeons are continuing to form larger groups, it is essential to select candidates who will contribute to a positive work environment. This article shows which traits may be the most valuable when selecting candidates and in which ways a selection committee may want to focus their search. Methods: For the study, the Canadian Society of Plastic Surgeons’ members answered a survey containing questions about demographics, the factors which influence the selection process, and their hiring experiences. Responses were separated and compared in groups based on gender, practice type, group size, and years practising. Significance was established if P < .05 using the χ 2 test. Results: The most and least important factors regarding hiring a new group member were established. Statistically significant results were obtained between several different factors, including hiring a non-Canadian, the importance of the candidate’s professional reputation, the number of publications by the candidate, and the presence or absence of program director letters. A majority (54%) of society members regret having hired a candidate, with the vast majority of these (75%) indicating personality and work ethic issues as opposite to professional skills as the uncomplimentary feature. Conclusion: This study has identified the key features which influence hiring new candidates. The need to develop a more efficient hiring process has been identified and has highlighted the difficulty faced by Canadian plastic surgery groups when recruiting new members.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".