Impact of research investment on scientific productivity of junior researchers
Bibliographic record
Abstract
There is a demand for providing evidence on the effectiveness of research investments on the promotion of novice researchers' scientific productivity and production of research with new initiatives and innovations. We used a mixed method approach to evaluate the funding effect of the New Investigator Fund (NIF) by comparing scientific productivity between award recipients and non-recipients. We reviewed NIF grant applications submitted from 2004 to 2013. Scientific productivity was assessed by confirming the publication of the NIF-submitted application. Online databases were searched, independently and in duplicate, to locate the publications. Applicants' perceptions and experiences were collected through a short survey and categorized into specified themes. Multivariable logistic regression was performed. Odds ratios (OR) with 95 % confidence intervals (CI) are reported. Of 296 applicants, 163 (55 %) were awarded. Gender, affiliation, and field of expertise did not affect funding decisions. More physicians with graduate education (32.0 %) and applicants with a doctorate degree (21.5 %) were awarded than applicants without postgraduate education (9.8 %). Basic science research (28.8 %), randomized controlled trials (24.5 %), and feasibility/pilot trials (13.3 %) were awarded more than observational designs (p < 0.001). Adjusting for applicants and application factors, awardees published the NIF application threefold more than non-awardees (OR = 3.4, 95 %, CI = 1.9, 5.9). The survey response rate was 90.5 %, and only 58 % commented on their perceptions, successes, and challenges of the submission process. These findings suggest that research investments as small as seed funding are effective for scientific productivity and professional growth of novice investigators and production of research with new initiatives and innovations. Further efforts are recommended to enhance the support of small grant funding programs.
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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 | 0.153 | 0.426 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".