Networks of trainees: examining the effects of attending an interdisciplinary research training camp on the careers of new obesity scholars
Bibliographic record
Abstract
Students training in obesity research, prevention, and management face the challenge of developing expertise in their chosen academic field while at the same time recognizing that obesity is a complex issue that requires a multidisciplinary and multisectoral approach. In appreciation of this challenge, the Canadian Obesity Network (CON) has run an interdisciplinary summer training camp for graduate students, new career researchers, and clinicians for the past 8 years. This paper evaluates the effects of attending this training camp on trainees' early careers. We use social network analysis to examine the professional connections developed among trainee Canadian obesity researchers who attended this camp over its first 5 years of operation (2006-2010). We examine four relationships (knowing, contacting, and meeting each other, and working together) among previous trainees. We assess the presence and diversity of these relationships among trainees across different years and disciplines and find that interdisciplinary contact and working relationships established at the training camp have been maintained over time. In addition, we evaluate the qualitative data on trainees' career trajectories and their assessments of the impact that the camp had on their careers. Many trainees report that camp attendance had a positive impact on their career development, particularly in terms of establishing contacts and professional relationships. Both the quantitative and the qualitative results demonstrate the importance of interdisciplinary training and relationships for career development in the health sciences.
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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 | 0.007 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".