Opinion leaders and changes over time: a survey
Bibliographic record
Abstract
BACKGROUND: Opinion leaders represent one way to disseminate new knowledge and influence the practice behaviors of physicians. This study explored the stability of opinion leaders over time, whether opinion leaders were polymorphic (i.e., influencing multiple practice areas) or monomorphic (i.e., influencing one practice area), and reach of opinion leaders in their local network. METHODS: We surveyed surgeons and pathologists in Ontario to identify opinion leaders for colorectal cancer in 2003 and 2005 and to identify opinion leaders for breast cancer in 2005. We explored whether opinion leaders for colorectal cancer identified in 2003 were re-identified in 2005. We examined whether opinion leaders were considered polymorphic (nominated in 2005 as opinion leaders for both colorectal and breast cancer) or monomorphic (nominated in 2005 for only one condition). Social-network mapping was used to identify the number of local colleagues identifying opinion leaders. RESULTS: Response rates for surgeons were 41% (2003) and 40% (2005); response rates for pathologists were 42% (2003) and 37% (2005). Four (25%) of the surgical opinion leaders identified in 2003 for colorectal cancer were re-identified in 2005. No pathology opinion leaders for colorectal cancer were identified in both 2003 and 2005. Only 29% of surgical opinion leaders and 17% of pathology opinion leaders identified in the 2005 survey were considered influential for both colorectal cancer and breast cancer. Social-network mapping revealed that only a limited number of general surgeons (12%) or pathologists (7%) were connected to the social networks of identified opinion leaders. CONCLUSIONS: Opinion leaders identified in this study were not stable over a two-year time period and generally appear to be monomorphic, with clearly demarcated areas of expertise and limited spheres of influence. These findings may limit the practicability of routinely using opinion leaders to influence practice.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".