Bibliographic record
Abstract
HYPOTHESIS: Financial incentives are the only form of compensation that will motivate surgeons at an academic health sciences center to perform the tasks outlined in the hospital's mission statement. DESIGN: A questionnaire divided into 5 sections: demographics, compensation, time allocation, benefits and incentives, and motivational factors. SETTING: The Department of Surgery, The Toronto Hospital, Toronto, Ontario. PARTICIPANTS: All academic surgeons (N=64) practicing at The Toronto Hospital in July 1997. RESULTS: Of the 64 eligible mailed surveys, there were responses for 59. Of these 59 surgeons, 48 (81%) receive compensation through a fee-for-service method. However, only 32 (54%) of the surgeons prefer the fee-for-service method, while 18 (31%) prefer salary and 9 (15%) prefer an alternative system. On average, these academic surgeons spend 44% of their time teaching or performing research, for which they receive 14% of their total income. Of the motivational factors assessed, financial bonuses are a positive motivational factor for all "surgeon tasks." In addition, task-specific motivational factors were established for research, teaching, and operating, including research facilities, mentorship and prestige, and interesting case types, respectively. CONCLUSIONS: Surgeons are not appropriately renumerated for time spent on academic activities, and many would prefer an alternative form of compensation to the fee-for-service method. Knowledge that surgeons are receptive to tasks supporting the hospital's mission statement leads us to conclude that appropriate motivation can shape the activity of academic surgeons. Financial rewards ranked the highest as a motivational factor for all surgeon tasks; however, task-specific motivational factors were identified. Overall, multiple factors, specifically targeted to the individual, will serve to motivate. Thus, compensation packages based on individual preferences and personal motivational factors will be the most successful.
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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 | 0.020 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 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".