Factors associated with cumulative research funding of investigators from CIHR – a major health-research funding agency
Bibliographic record
Abstract
PURPOSE: Few systematic studies have focused on determinants of cumulative research funding (CRF), a measure of research productivity among career researchers world-wide. Using researchers funded by the Canadian Institutes for Health Research (CIHR), this exploratory study quantifies the association between CIHR-investigator roles and CRF obtained from the CIHR. METHODS: CIHR grants and awards obtained in fiscal years 1999-2006 by non-trainee principal investigators (PI) and by supervisors of CIHR trainee awards were used to determine investigator-level CRF. Log-transformed CRF was regressed on investigator role as CIHR-salary award recipient or supervisor of CIHR-trainee award recipients after adjustment for number of project-years, research area, and PI status. Number of publications in life sciences and biomedical journals from January 2000 to August 2007 was compared among 80 randomly-selected CIHR-investigators who were supervisors (n=40) and non-supervisors (n=40). Reported results were considered significant at P-value < 0.05. RESULTS: Multiple regression analysis based on 6515 CIHR-investigators indicated that salary award recipients were associated with a 29.5% higher CRF but the magnitude of this positive association was inversely associated with time since first receiving salary award. Supervisors were associated with, on average, a 13.1% decrease in CRF; increasing numbers of trainees supervised was associated with decreasing CRF. Earlier recipients of grants and salary awards were more likely also supervisors. The median number of publications for supervisors was higher than that for non-supervisors (31 vs. 11.5). CONCLUSIONS: Demonstrated associations between investigator-level CRF and predictors suggest that CRF should be considered in the context of investigators' multiple roles. The study, without establishing causality, also documents evidence of multi-lateral returns to the enterprise of academic research from salary and trainee awards. The major limitation is that CRF and predictors are derived from a single funding agency. Findings should be viewed as preliminary and should serve to develop hypotheses for future, comprehensive research.
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 | 0.009 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".