Health promotion and knowledge translation: two roads to the same destination?
Bibliographic record
Abstract
Health Promotion (HP), a continuously evolving field, is riddled with complexities as experts and community members develop new approaches to researching social behaviours, addressing health concerns and advocating for the values of equity, empowerment and healthy public policy. Similarly complex is the field of knowledge translation (KT), putting research into action for the purpose of changing behaviours, policy and practice. Similar values, methods and techniques govern these two practices. This paper is based on a series of discussions between two young professionals who found themselves navigating the complexities of HP and KT and attempting to understand their chosen fields of practice. The discussions considered such issues as discipline-based silos, the use and purpose of new terminologies and languages in research, and whether or not existing practices are simply being renamed or branded in order to appear innovative and new.
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 | 0.115 | 0.125 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.015 | 0.104 |
| Scholarly communication | 0.049 | 0.100 |
| Open science | 0.004 | 0.036 |
| Research integrity | 0.023 | 0.027 |
| Insufficient payload (model declined to judge) | 0.019 | 0.006 |
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".