Reflecting on backward design for knowledge translation Comment on "A call for a backward design to knowledge translation"
Bibliographic record
Abstract
In a recent Editorial for this journal, El-Jardali and Fadlallah proposed a new framework for Knowledge Translation (KT) in healthcare. Many such frameworks already exist; thus, new entrants to the field must be scrutinized in regard to their unique contributions to advancing understanding and practice. The El-Jardali and Fadlallah framework focuses on policy-level discussions, a relatively under-studied issue to date. Their framework usefully incorporates both priority setting questions at the front-end (which KT efforts get undertaken and which do not) as well as evaluation questions at the back-end (how do we show that more evidence-informed decisions are actually better ones?). Their framework also emphasizes capacity building among both decision-makers and researchers. This is an area worthy of additional attention, particularly because it is likely to be far more challenging than El-Jardali and Fadlallah allow.
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 | 0.045 | 0.173 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.015 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.072 | 0.099 |
| Insufficient payload (model declined to judge) | 0.006 | 0.005 |
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".