Reported use of implementation science theories, models, and frameworks in 151 implementation trials: secondary analysis of a systematic review targeting nursing practice
Bibliographic record
Abstract
BACKGROUND: Theories, models, and frameworks (TMFs) are central to the development and evaluation of implementation strategies supporting evidence-based practice (EBP). However, evidence on how and to what extent TMFs are used in implementation trials remains limited. PURPOSE: This study aimed to examine the nature and extent of TMF use in implementation trials, identify which TMFs are most frequently employed, and explore temporal trends in their use. METHODS: A secondary analysis was conducted on 151 randomized trials of implementation strategies targeting EBP in nursing. Trials and their protocols were coded in NVivo 14 using a framework adapted from Painter's continuum of theory use (2005) and Michie and Prestwich's theory coding scheme (2010). The framework categorized theory use as "informed by," "applied," "tested," or "built" theory. Descriptive statistics were calculated in R, and temporal trends in TMF use across categories were analyzed. RESULTS: Among the 151 trials, 54 (36%) reported using a TMF. Of these, most applied TMFs to guide implementation strategy design (28%), followed by justifying the study's purpose, aims, or objectives (15%). Testing theory was infrequent (9%), and no trials reported refining or building theory. Classic theories, such as the theory of planned behavior and social cognitive theory, were the most frequently cited. No clear temporal trend was found in TMF use across the categories. CONCLUSIONS: TMFs remain underutilized in implementation trials, with their application primarily limited to justifying study rationale or informing implementation strategy development. Greater emphasis on the testing and refinement of TMFs is recommended to advance implementation science. REGISTRATION INFORMATION: Review registration: PROSPERO CRD42019130446.
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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 | 0.416 | 0.741 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.010 | 0.011 |
| Bibliometrics | 0.025 | 0.028 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".