Sustainability of knowledge translation interventions in healthcare decision-making: a scoping review
Bibliographic record
Abstract
BACKGROUND: Knowledge translation (KT, also known as research utilization, and sometimes referring to implementation science) is a dynamic and iterative process that includes the synthesis, dissemination, exchange, and ethically sound application of knowledge to improve health. A KT intervention is one which facilitates the uptake of research. The long-term sustainability of KT interventions is unclear. We aimed to characterize KT interventions to manage chronic diseases that have been used for healthcare outcomes beyond 1 year or beyond the termination of initial grant funding. METHODS: We conducted a scoping review by searching MEDLINE, Embase, Cochrane Central Register of Controlled Trials (CENTRAL), Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Campbell from inception until February 2013. We included experimental, quasi-experimental, and observational studies providing information on the sustainability of KT interventions for managing chronic diseases in adults and focusing on end-users including patients, clinicians, public health officials, health service managers, and policy-makers. Articles were screened and abstracted by two reviewers, independently. The data were charted and results described narratively. RESULTS: We included 62 studies reported in 103 publications (total 260,688 patients) plus 41 companion reports after screening 12,328 titles and abstracts and 464 full-text articles. More than half of the studies were randomized controlled trials (RCTs). The duration of the KT intervention ranged from 61 to 522 weeks. Nine chronic conditions were examined across the studies, such as diabetes (34 %), cardiovascular disease (28 %), and hypertension (16 %). Thirteen KT interventions were reported across the studies. Patient education was the most commonly examined (20 %), followed by self-management (17 %). Most studies (61 %) focused on patient-level outcomes (e.g. disease severity), while 31 % included system-level outcomes (e.g. number of eye examinations), and 8 % used both. The interventions were aimed at the patient (58 %), health system (28 %), and healthcare personnel (14 %) levels. CONCLUSIONS: We found few studies focusing on the sustainability of KT interventions. Most of the included studies focused on patient-level outcomes and patient-level KT interventions. A future systematic review can be conducted of the RCTs to examine the impact of sustainable KT interventions on health outcomes.
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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 | 0.210 | 0.467 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.013 |
| Bibliometrics | 0.036 | 0.037 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.015 | 0.018 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| 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".