Selecting and tailoring implementation interventions: a concept mapping approach
Bibliographic record
Abstract
Abstract Background To improve the uptake of research into practice, knowledge translation frameworks recommend tailoring implementation strategies to address practice barriers. This study reports our experience pairing the Theoretical Domains Framework with information from multiple stakeholder groups to co-develop practice-informed strategies for improving the implementation of an evidence-based outcome measurement tool across a large community health system for preschoolers with communication impairments. Methods Concept mapping was used to identify strategies for improving implementation of the Focus on the Outcomes of Communication Under Six (FOCUS) in Ontario Canada’s Preschool Speech and Language Program. This work was done in five stages. First, we interviewed 37 speech-language pathologists (clinicians) who identified 90 unique strategies to resolve practice barriers to FOCUS implementation. Second, clinicians (n = 34), policy-makers (n = 3), and members of the FOCUS research team (n = 6) sorted and rated the strategies by importance and feasibility. Third, stakeholders’ sorting data were analyzed to generate a two-dimensional concept map. Based on the rating data from stakeholders, we prioritized a list of strategies that were rated as highly important and highly feasible, and summarized the practice barriers addressed by each of the prioritized strategies. Fourth, we validated these findings with stakeholders via an online survey. Fifth, the mechanisms of action of the prioritized list of strategies were considered based on available evidence from the Theoretical Domains Framework and associated behavior change literature. Results Stakeholders categorized the 90 unique implementation strategies into a six-cluster concept map. Based on stakeholders’ ratings, a list of 14 implementation strategies were prioritized. These implementation strategies were reported to resolve barriers within the environmental context and resources and beliefs about consequences domains of the Theoretical Domains Framework. All but one of the prioritized strategies have a demonstrated link in resolving existing barriers according to the behavioral change literature. Conclusions Our study contributes to a growing literature that demonstrates the process of tailoring implementation strategies to specific barriers. Practical drawbacks and benefits of using concept mapping as a way to engage stakeholders in implementation research are discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.113 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.009 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.006 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| 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".