Building strong health and career trajectories through translational research
Bibliographic record
Abstract
Translational research (TR) is the movement of fundamental scientific discoveries into healthcare settings and population health policy, and parallels the goals of DOHaD research. Unfortunately, there is little guidance on how to become a translational researcher. To understand the opinions of DOHaD trainees towards TR, we conducted a workshop at the DOHaD World Congress 2022. We found that trainees were enthusiastic for their work to have translational impact, and that they feel that holistic, multidisciplinary solutions may lead to more generalisable research. However, there lacks support for TR career pathways, which may stall the execution of the long-term vision of the DOHaD agenda. We put forward recommendations for trainees to clarify their purpose in pursuing TR and for seeking relevant people and patronages to support their training paths. For mentors, training institutions, and scientific societies, we recommend developing TR-specific programmes, and implementing training opportunities, networking events, and funding to support these endeavours.
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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 | 0.115 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.013 |
| Scholarly communication | 0.023 | 0.018 |
| Open science | 0.004 | 0.041 |
| Research integrity | 0.007 | 0.016 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".