A scoping review of theories, models and frameworks used or proposed to evaluate knowledge mobilization strategies
Bibliographic record
Abstract
Abstract Background Evaluating knowledge mobilization strategies (KMb) presents challenges for organizations seeking to understand their impact to improve KMb effectiveness. Moreover, the large number of theories, models, and frameworks (TMFs) available can be confusing for users. Therefore, the purpose of this scoping review was to identify and describe the characteristics of TMFs that have been used or proposed in the literature to evaluate KMb strategies. Methods A scoping review methodology was used. Articles were identified through searches in electronic databases, previous reviews and reference lists of included articles. Titles, abstracts and full texts were screened in duplicate. Data were charted using a piloted data charting form. Data extracted included study characteristics, KMb characteristics, and TMFs used or proposed for KMb evaluation. An adapted version of Nilsen (Implement Sci 10:53, 2015) taxonomy and the Expert Recommendations for Implementing Change (ERIC) taxonomy (Powell et al. in Implement Sci 10:21, 2015) guided data synthesis. Results Of the 4763 search results, 505 were retrieved, and 88 articles were eligible for review. These consisted of 40 theoretical articles (45.5%), 44 empirical studies (50.0%) and four protocols (4.5%). The majority were published after 2010 (n = 70, 79.5%) and were health related (n = 71, 80.7%). Half of the studied KMb strategies were implemented in only four countries: Canada, Australia, the United States and the United Kingdom (n = 42, 47.7%). One-third used existing TMFs (n = 28, 31.8%). According to the adapted Nilsen taxonomy, process models (n = 34, 38.6%) and evaluation frameworks (n = 28, 31.8%) were the two most frequent types of TMFs used or proposed to evaluate KMb. According to the ERIC taxonomy, activities to “train and educate stakeholders” (n = 46, 52.3%) were the most common, followed by activities to “develop stakeholder interrelationships” (n = 23, 26.1%). Analysis of the TMFs identified revealed relevant factors of interest for the evaluation of KMb strategies, classified into four dimensions: context, process, effects and impacts. Conclusions This scoping review provides an overview of the many KMb TMFs used or proposed. The results provide insight into potential dimensions and components to be considered when assessing KMb strategies.
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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 | 0.124 | 0.305 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.063 | 0.050 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".