1046 The canadian injury prevention trainee network: building capacity for the future of injury prevention research
Bibliographic record
Abstract
Background Injuries are a serious but preventable health concern in Canada and a growing field of research, attracting a large number of graduate students and other trainees across Canada to identify injury prevention as their field of study. In 2009, the Canadian Institute for Health Research funded a team in Child and Youth (C&Y) Injury Prevention. With this support, the team was able to develop a model of practice involving researchers, stakeholders, knowledge users, and trainees as part of a multidisciplinary approach to reducing the burden of injury in youth. The C&Y team was successful at supporting and highlighting the work of over 40 team trainees. The efforts of this trainee team have resulted in the formation of the Canadian Injury Prevention Trainee Network (CIPTN). Objective The CIPTN aims to build a network of trainees interested in the science and practice of injury prevention (IP) from a multi-disciplinary perspective. Specifically, the goals include increasing opportunities for collaboration, professional development, mentorship and networking. Results The CIPTN has worked to develop a list of learning and research oriented catalyst activities for trainee members. The CIPTN has successfully collaborated with IP experts and organisations from across Canada in the development of a comprehensive IP resource, as well as updating the Canadian Injury Prevention Curriculum, a course for IP practitioners across Canada. Further, an executive board and governance structure has facilitated the identification of future collaborative activities (e.g., bi-monthly seminars, grant writing, and evaluating an injury methods workshop). Conclusions The CIPTN increases opportunities for junior researchers and trainees to work within a network of colleagues to share research, build collaborative projects, expand capacity, and provide training opportunities. This in turn, will develop the quantity and quality of IP science and practice across Canada for future generations.
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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 | 0.056 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.022 | 0.011 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.007 | 0.022 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.042 | 0.010 |
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".