Knowledge Translation Practices Of Health Services Research Organizations In The United States
Bibliographic record
Abstract
Health services research organizations have generated a growing body of literature that focuses on better understanding challenges facing health care delivery. However, their findings do not always reach end users (e.g., policymakers, providers, managers, general public) in ways that are helpful, relevant, or cost-effective despite the availability of numerous resources designed to aid researchers in communicating more effectively. The purpose of this study was to understand better how health services research organizations in the United States communicate their research findings to end users; determine the degree to which they are translating research findings in ways consistent with the empirical evidence; and determine whether organizational characteristics such as university affiliation, organizational specialty, or size explain any variation in responses. Leaders of health services research organizations in the United States responded to a survey about their organizations' knowledge translation practices. The survey instrument and knowledge translation framework were based largely on work conducted by Lavis, Robertson, Woodside, McLeod, and Abelson (2003a) in Canada. Findings from this empirical study expanded the Lavis et al. (2003a) study by setting a baseline for knowledge translation practices, across the research continuum, for health services research organizations in the United States. The data showed that health services research organizations largely communicate about their research in the same manner, regardless of university affiliation, organizational specialty, or size. Research organizations conduct knowledge translation activities throughout the course of their research projects, although in many cases there are gaps between what the literature suggests research organizations optimally should be doing and what they report doing. Notably, these gaps include evaluating knowledge translation activities, utilizing social media tools to extend messaging to end users, engaging with end users throughout the research process, building expectations for knowledge translation into policies and procedures, and investing in knowledge translation development at the organizational level. The findings suggest areas of improvement for health services research organizations. This study observes, however, that increasing knowledge translation capacity will require a cultural shift, and increased collaboration, across the health services research community. Accordingly, this study recommends several action steps. Specifically, health services research organizations should develop knowledge translation expectations through organizational policies and procedures, and invest in capacity building, including training research staff or working with knowledge brokers. Funders should include expectations for knowledge translation in projects, and universities might consider updated promotion and tenure systems that acknowledge and reward translation activities. Bolstering knowledge translation practices as identified in this study, and using the baseline data as a measuring point to evaluate future interventions, contributes to end users successfully receiving research findings in ways that can be useful for decision making, ultimately enhancing the quality of health and health care.
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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 | 0.010 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".