Identifying educationally influential specialists: Issues arising from the use of “classic” criteria
Bibliographic record
Abstract
INTRODUCTION: Educationally influential physicians (EIPs) are identified by their colleagues as people who (1) encourage learning and enjoy sharing their knowledge, (2) are clinical experts and always seem up to date, and (3) treat others as equals. We aimed to identify surgical and pathologist EIPs for colorectal cancer (CRC) in Ontario as part of a blended knowledge transfer program. METHODS: A population-based cohort of surgeons (n = 794) and pathologists (n = 449) were sent surveys modeled on the Hiss method for identifying EIPs. Four formal mailings (including incentives) and telephone calls and faxes were completed. This labor-intensive process identified "general" EIPs and surgery or pathology EIPs for CRC. The characteristics of EIPs in these groups were studied. RESULTS: The response rate was 41% for surgeons and 42% for pathologists. One hundred eighteen general EIPs were identified and substantially more surgical EIPs for CRC (n = 63) than pathology EIPs for CRC (n = 6) were recognized. Forty-two of 81 medical centers in Ontario identified an EIP We also identified a cohort of "domain experts, " physicians whose opinion was valued for CRC but who did not meet the Hiss EIP criteria. This cohort of "domain experts" was larger than the cohort of ElPs for CRC for both surgeons (63 vs. 154) and pathologists (6 vs. 154). DISCUSSION: In this population study, we identified EIPs for CRC using the Hiss method, although significantly more surgical than pathology EIPs for CRC were recognized. The educational influence of domain experts who do not fulfill the Hiss characteristics compared with EIPs for CRC remains to be determined.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.202 | 0.428 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| 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".