What if implementation is not the problem? Exploring the missing links between knowledge and action
Bibliographic record
Abstract
Given all the available knowledge about effective implementation, why do many organizations continue to have-or appear to have-an implementation problem? Analysis of a 7-year corpus of reports by a Canadian health region's "embedded" research and evaluation unit sought to discover the source of the region's intractable difficulty implementing improvement. Findings suggested that the problem was neither a lack of knowledge (decision-makers displayed sophisticated understanding of fundamental issues) nor an inability to take action (there existed sufficient capacity to implement change). However, managers' high-level knowledge was not made actionable, and micro-level decision-making often produced piecemeal actions inadequately informed by existing knowledge. The problem arose at the stage of "operationalization"-the identification of concrete, executable actions fully informed by knowledge of complex, system-level issues. Yet this crucial phase is a focus of neither the implementation nor knowledge translation (KT) literatures. The organizational decision-making literature reveals how decision-makers initiate operationalization (i.e., by setting the direction for a discovery approach) but not how they can ensure its successful completion. The focus of KT research and practice should expand to explicating and improving decision-making, lest KT become an exercise of infusing content into a broken process. Copyright © 2014 John Wiley & Sons, Ltd.
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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 | 0.079 | 0.167 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.008 | 0.047 |
| Scholarly communication | 0.027 | 0.041 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".