Unfolding and characterizing the barriers and facilitators of scaling-up evidence-based interventions from the stakeholders’ perspective: a concept mapping approach
Bibliographic record
Abstract
INTRODUCTION AND AIMS: Much attention has been paid to scaling-up evidence-based interventions (EBIs) in previous implementation science studies. However, there is limited research on how stakeholders perceive factors of the scaling-up of EBIs. This study aimed to identify the barriers and facilitators of scaling-up the nurse-led evidence-based practice of post-stroke dysphagia identification and management (EBP-PSDIM) from the stakeholders' perspective, and to assess their importance and feasibility. METHODS: This study was conducted using concept mapping. Through purposive sampling, 18 stakeholders were recruited for brainstorming in which they responded to the focus prompt. Here, statements regarding perceived barriers and facilitators to EBI scaling-up were elicited and then sorted by similarity before being rated based on the importance and feasibility. Cluster analysis, multidimensional scaling, and descriptive statistics were utilized to analyze the data. RESULTS: Ultimately, 61 statements perceived to influence the scaling-up were grouped into four primary clusters, that is, community-related factors, resource team-related factors, evidence-based practice program-related factors, and scaling-up strategy-related factors. The 'perceived needs of the community' was rated as the most important and feasible factor to address, whereas 'costs/resource mobilization' was rated as the least important and feasible one. CONCLUSION: From the stakeholders' perspective, factors involved in the EBP-PSDIM program scaling-up were initially validated as being multidimensional and conceptually distinct;The importance and feasibility ratings of the barriers and facilitators could be used to help decision-makers to prioritize the most appropriate factors to be considered when developing implementation strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".