Knowledge translation strategies used for sustainability of an evidence-based intervention in child health: a multimethod qualitative study
Bibliographic record
Abstract
BACKGROUND: Sustainability of evidence-based interventions (EBIs) is suboptimal in healthcare. Evidence on how knowledge translation (KT) strategies are used for the sustainability of EBIs in practice is lacking. This study examined what and how KT strategies were used to facilitate the sustainability of Alberta Family Integrated Care (FICare)™, a psychoeducational model of care scaled and spread across 14 neonatal intensive care units, in Alberta, Canada. METHODS: First, we conducted an environmental scan of relevant documents to determine the use of KT strategies to support the sustainability of Alberta FICare™. Second, we conducted semi-structured interviews with decision makers and operational leaders to explore what and how KT strategies were used for the sustainability of Alberta FICare™, as well as barriers and facilitators to using the KT strategies for sustainability. We used the Expert Recommendations for Implementation Change (ERIC) taxonomy to code the strategies. Lastly, we facilitated consultation meetings with the Alberta FICare™ leads to share and gain insights and clarification on our findings. RESULTS: We identified nine KT strategies to facilitate the sustainability of Alberta FICare™: Conduct ongoing training; Identify and prepare local champions; Research co-production; Remind clinicians; Audit and provide feedback; Change record systems; Promote adaptability; Access new funding; and Involve patients/consumers and family members. A significant barrier to the sustainability of Alberta FICare™ was a lack of clarity on who was responsible for the ongoing maintenance of the intervention. A key facilitator to sustainability of Alberta FICare was its alignment with the Maternal, Newborn, Child & Youth Strategic Clinical Network (MNCY SCN) priorities. Co-production between researchers and health system partners in the design, implementation, and scale and spread of Alberta FICare™ was critical to sustainability. CONCLUSION: This research highlights the importance of clearly articulating who is responsible for continued championing for the sustainability of EBIs. Additionally, our research demonstrates that the adaptation of interventions must be considered from the onset of implementation so interventions can be tailored to align with contextual barriers for sustainability. Clear guidance is needed to continually support researchers and health system leaders in co-producing strategies that facilitate the long-term sustainability of effective EBIs in practice.
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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 | 0.018 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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".