Bench, Bedside, Curbside, and Home: Translational Research to Include Transformative Change Using Educational Research
Bibliographic record
Abstract
Translational research originated in the medical field during the 1990s to describe taking discovery based research through the steps of applying it to clinical research and patient-oriented care. This model is implicitly linear, depicting the flow of information from researchers’ bench, to a clinical trial bedside, to a primary care physician’s practice. The prevailing model of translational research, referred to as “Bench to Bedside to Curbside,” is limited in that it does not adequately incorporate stakeholders outside of the professional or research community because Curbside refers to physician care delivered to patients. This omits the transformative impact that research can have on the general populace if implemented through educational research, disseminating knowledge to people who can use it. In this article we argue that a fourth category needs to be incorporated into the previous T1-T3 Bench to Bedside to Curbside model, and this fourth category represents T4, “Home.” We seek to further define and describe, while providing a new model for translational research that is more circular in nature and inclusive of the general populace. We also suggest that the incorporation of educational researchers and practitioners would expand the current collaborative nature of translational research and is a way to expand the translational model. This promises more adequate, effective, and sustainable impacts on a target population.
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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 | 0.071 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.008 | 0.094 |
| Scholarly communication | 0.021 | 0.036 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".