Putting research in place: an innovative approach to providing contextualized evidence synthesis for decision makers
Bibliographic record
Abstract
BACKGROUND: The Contextualized Health Research Synthesis Program (CHRSP), developed in 2007 by the Newfoundland and Labrador Centre for Applied Health Research, produces contextualized knowledge syntheses for health-system decision makers. The program provides timely, relevant, and easy-to-understand scientific evidence; optimizes evidence uptake; and, most importantly, attunes research questions and evidence to the specific context in which knowledge users must apply the findings. METHODS: As an integrated knowledge translation (KT) method, CHRSP: Involves intensive partnerships with senior healthcare decision makers who propose priority research topics and participate on research teams; Considers local context both in framing the research question and in reporting the findings; Makes economical use of resources by utilizing a limited number of staff; Uses a combination of external and local experts; and Works quickly by synthesizing high-level systematic review evidence rather than primary studies. Although it was developed in the Canadian province of Newfoundland and Labrador, the CHRSP methodology is adaptable to a variety of settings with distinctive features, such as those in rural, remote, and small-town locations. RESULTS: CHRSP has published 25 syntheses on priority topics chosen by the provincial healthcare system, including: Clinical and cost-effectiveness: telehealth, rural renal dialysis, point-of-care testing; Community-based health services: helping seniors age in place, supporting seniors with dementia, residential treatment centers for at-risk youth; Healthcare organization/service delivery: reducing acute-care length of stay, promoting flu vaccination among health workers, safe patient handling, age-friendly acute care; and Health promotion: diabetes prevention, promoting healthy dietary habits. These studies have been used by decision makers to inform local policy and practice decisions. CONCLUSIONS: By asking the health system to identify its own priorities and to participate directly in the research process, CHRSP fully integrates KT among researchers and knowledge users in healthcare in Newfoundland and Labrador. This high level of decision-maker buy-in has resulted in a corresponding level of uptake. CHRSP studies have directly informed a number of policy and practice directions, including the design of youth residential treatment centers, a provincial policy on single-use medical devices, and most recently, the opening of the province's first Acute Care for the Elderly hospital unit.
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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 | 0.243 | 0.465 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".