A realist review of interventions and strategies to promote evidence-informed healthcare: a focus on change agency
Bibliographic record
Abstract
BACKGROUND: Change agency in its various forms is one intervention aimed at improving the effectiveness of the uptake of evidence. Facilitators, knowledge brokers and opinion leaders are examples of change agency strategies used to promote knowledge utilization. This review adopts a realist approach and addresses the following question: What change agency characteristics work, for whom do they work, in what circumstances and why? METHODS: The literature reviewed spanned the period 1997-2007. Change agency was operationalized as roles that are aimed at effecting successful change in individuals and organizations. A theoretical framework, developed through stakeholder consultation formed the basis for a search for relevant literature. Team members, working in sub groups, independently themed the data and developed chains of inference to form a series of hypotheses regarding change agency and the role of change agency in knowledge use. RESULTS: 24, 478 electronic references were initially returned from search strategies. Preliminary screening of the article titles reduced the list of potentially relevant papers to 196. A review of full document versions of potentially relevant papers resulted in a final list of 52 papers. The findings add to the knowledge of change agency as they raise issues pertaining to how change agents' function, how individual change agent characteristics effect evidence-informed health care, the influence of interaction between the change agent and the setting and the overall effect of change agency on knowledge utilization. Particular issues are raised such as how accessibility of the change agent, their cultural compatibility and their attitude mediate overall effectiveness. Findings also indicate the importance of promoting reflection on practice and role modeling. The findings of this study are limited by the complexity and diversity of the change agency literature, poor indexing of literature and a lack of theory-driven approaches. CONCLUSION: This is the first realist review of change agency. Though effectiveness evidence is weak, change agent roles are evolving, as is the literature, which requires more detailed description of interventions, outcomes measures, the context, intensity, and levels at which interventions are implemented in order to understand how change agent interventions effect evidence-informed health care.
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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 | 0.062 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.025 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".