Strategic faculty recruitment increases research productivity within an academic university division.
Bibliographic record
Abstract
BACKGROUND: Research is an important mandate for academic surgical divisions. However, there is widespread concern that the current health care climate is leading to a decline in research activity. A University of British Columbia (UBC) academic surgical division attempted to address this concern by strategically recruiting PhD research scientists to prioritize research and develop collaborative research programs. The objective of our study was to determine whether this strategy resulted in increased research productivity. METHODS: We reviewed the UBC Department of Surgery database to assess research funding obtained by the Division of General Surgery for the years 1994-2004. We searched MEDLINE for peer-reviewed publications by faculty members during this period. RESULTS: Research funding increased from a mean of Can$417,292 per year in the 5 years (1994/95-1998/99) before the recruitment of dedicated PhD scientists to a mean of Can$1.3 million per year in the 5 years following the recruitment strategy (1999/2000-2003/04; p = 0.012). Funding for the initial 5 years was Can$2.1 million, including 1 Canadian Institutes of Health Research (CIHR) grant. Funding increased to Can$6.8 million, including 22 CIHR grants over the subsequent 5 years (p < 0.001). Collaborative research led to the awarding of multidisciplinary grants exceeding Can$4 million with divisional members as principle or coprinciple investigators. From 1994/05 to 1998/99, the total number of peer-reviewed publications was 116 (mean 23.2, standard deviation [SD] 7 per year), increasing to 144 from 1999/2000 to 2003/04 (mean 28.8, SD 13 per year). The trend was for publications in journals with higher impact factors in the latter 5-year period. CONCLUSION: Strategic recruitment resulted in increased and sustained research productivity. Interactions between research scientists and clinicians resulted in successful program grant funding support. These results have implications for sustaining the research mission within academic departments of surgery.
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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 | 0.071 | 0.176 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".