Fostering translational research in chronic disease management: a logic model proposal
Bibliographic record
Abstract
Abstract Translational research aims at reducing gaps between fundamental scientific discoveries and real-world applications. However, the trajectory of most scientific discoveries along the translation research continuum remains highly complex. Logic models are powerful tools that can help reduce this complexity. They are often used to lay out road maps and depict the relationship between activities and their intended effects. Few if any existing tools have been designed to guide the implementation and evaluation of collaborative models between community-based primary health care and biomedical research. To address this gap, we developed a logic model in two stages: 1) a literature review; and 2) the drafting and revision of the model by experts in the field. We describe its components, including objectives, inputs, activities, target groups, outputs, and results for a collaborative model involving fundamental biomedical research and primary health care practices. Our proposed logic model provides a road map that has the potential to reduce the complexity faced by translational research in chronic diseases by providing guidelines for decision-makers. Future work should attempt to validate the model before its broad-based implementation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.057 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.014 | 0.014 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".