Sustaining knowledge translation interventions for chronic disease management in older adults: protocol for a systematic review and network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Failure to sustain knowledge translation (KT) interventions impacts patients and health systems, diminishing confidence in future implementation. Sustaining KT interventions used to implement chronic disease management (CDM) interventions is of critical importance given the proportion of older adults with chronic diseases and their need for ongoing care. Our objectives are to (1) complete a systematic review and network meta-analysis of the effectiveness and cost-effectiveness of sustainability of KT interventions that target CDM for end-users including older patients, clinicians, public health officials, health services managers and policy-makers on health care outcomes beyond 1 year after implementation or the termination of initial project funding and (2) use the results of this review to complete an economic analysis of the interventions identified to be effective. METHODS: For objective 1, comprehensive searches of relevant electronic databases (e.g. MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials), websites of health care provider organisations and funding agencies will be conducted. We will include randomised controlled trials (RCTs) examining the impact of a KT intervention targeting CDM in adults aged 65 years and older. To examine cost, economic studies (e.g. cost, cost-effectiveness analyses) will be included. Our primary outcome will be the sustainability of the delivery of the KT intervention beyond 1 year after implementation or termination of study funding. Secondary outcomes will include behaviour changes at the level of the patient (e.g. symptom management) and clinician (e.g. physician test ordering) and health system (e.g. cost, hospital admissions). Article screening, data abstraction and risk of bias assessment will be completed independently by two reviewers. Using established methods, if the assumption of transitivity is valid and the evidence forms a connected network, Bayesian random-effects pairwise and network meta-analysis will be conducted. For objective 2, we will build a decision analytic model comparing effective interventions to estimate an incremental cost-effectiveness ratio. DISCUSSION: Our results will inform knowledge users (e.g. patients, clinicians, policy-makers) regarding the sustainability of KT interventions for CDM. Dissemination plan of our results will be tailored to end-users and include passive (e.g. publications, website posting) and interactive (e.g. knowledge exchange events with stakeholders) strategies. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD42018084810.
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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 | 0.049 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.006 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".