ONF Trainee Awards Contribute to Capacity Building in Neurotrauma
Bibliographic record
Abstract
OBJECTIVE: Injury to the brain and spinal cord is one of the most catastrophic and costly occurrences in the Ontario health system. The objective of the present study was to evaluate the impact of past Ontario Neurotrauma Foundation (ONF) studentships and fellowships in terms of capacity building in the neurotrauma field in Ontario. METHOD: An online, cross sectional survey amongst past recipients of studentships and fellowships that terminated prior to July 2005. Explicit data were collected on various aspects of career development including current activity, awards and publications. RESULTS: Thirty-six out of 42 (86%) eligible past trainees responded; 12 (33%) were Masters students, 12 (33%) were PhD students and 12 (33%) were Post-Doctoral students. A majority of the recipients (61%) are currently involved in neurotrauma-related activities (clinical, research and teaching) in more than 20% of their time, with no substantial differences between the degree groups. Half the recipients are currently involved in neurotrauma-related research in more than 20% of their time. The awardees published 1.5 peer-review manuscripts/person-year and received multiple awards. A high majority of our recipients (86%) feel that the ONF award had a substantial impact on their career. CONCLUSIONS: A high proportion of past award recipients remain involved in neurotrauma activities, especially in research. These results may lead to a cautious conclusion of the positive impact of the ONF studentships and fellowships on neurotrauma capacity building. These results should be considered in strategic planning of funding agencies similar to ONF.
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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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".