Building theories of knowledge translation interventions: Use the entire menu of constructs
Bibliographic record
Abstract
BACKGROUND: In the ongoing effort to develop and advance the science of knowledge translation (KT), an important question has emerged around how theory should inform the development of KT interventions. DISCUSSION: Efforts to employ theory to better understand and improve KT interventions have until recently mostly involved examining whether existing theories can be usefully applied to the KT context in question. In contrast to this general theory application approach, we propose a 'menu of constructs' approach, where individual constructs from any number of theories may be used to construct a new theory. By considering the entire menu of available constructs, rather than limiting choice to the broader level of theories, we can leverage knowledge from theories that would never on their own provide a complete picture of a KT intervention, but that nevertheless describe components or mechanisms relevant to it. We can also avoid being forced to adopt every construct from a particular theory in a one-size-fits-all manner, and instead tailor theory application efforts to the specifics of the situation. Using audit and feedback as an example KT intervention strategy, we describe a variety of constructs (two modes of reasoning, cognitive dissonance, feed forward, desirable difficulties and cognitive load, communities of practice, and adaptive expertise) from cognitive and educational psychology that make concrete suggestions about ways to improve this class of intervention. SUMMARY: The 'menu of constructs' notion suggests an approach whereby a wider range of theoretical constructs, including constructs from cognitive theories with scope that makes the immediate application to the new context challenging, may be employed to facilitate development of more effective KT interventions.
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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 | 0.058 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.003 | 0.033 |
| Scholarly communication | 0.011 | 0.024 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 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".