SUPPORTING ABSORPTIVE CAPACITY FOR KNOWLEDGE BROKERS: EVIDENCE OF CANADIAN HEALTH ORGANIZATIONS
Bibliographic record
Abstract
The importance of using healthcare evidence by policy-makers is widely recognized [Lavis (2006), Ward et al. (2009)]. For over a decade, several strategies to improve the use of knowledge by policy-makers have been promoted [Landry et al. (2006); Amara et al. (2004)]. Among them, the use of individuals called "intermediaries" or "knowledge brokers" is presented as a potential strategy [Hargadon (2002); Lomas (2007)]. Situated at the organizational interface, these actors benefit from a strategic position allowing easier access to external knowledge [Cohen and Levinthal (1990)]. Therefore, they must develop good skills to be able to properly enjoy all opportunities to create value for their organization. In fact, many authors consider brokers as true knowledge integrators that assess, interpret, synthesize, exploit and transfer pertinent knowledge. Despite the presence of several studies that stress the importance of the multifaceted role of brokers, few have explored how these actors concretely integrate or absorb knowledge and especially, what skills are necessary for the success of their activities. The aims of this paper are: to propose a new conceptual model on research integration by knowledge brokers and to provide an empirical testing of this proposed model. The conceptual framework to be presented in this study builds on recent theoretical developments on the concept of knowledge absorptive capacity [Todorova and Durisin (2007)]. To test the conceptual framework, we collected survey data. The sample of 297 respondents was composed of professionals and managers involved at different levels of health services in Canada. To be eligible, respondents had to be engaged in knowledge brokering activities. Data analysis allowed presenting a first portrait of the profile of knowledge brokers working in health organizations in Canada. In this perspective, several descriptive analyses, such as the distribution of knowledge brokers according to their membership organizations, their status, education, experience, etc., were completed. Other confirmatory analyses with EQS were completed to confirm the theoretical validity of the dimensions of the broker's absorptive capacity. Finally, bivariate analyses were used with these dimensions to compare knowledge brokers regarding their absorptive capacity and the explanatory variables documented in the literature. In the last part of this paper, we discuss the implications of the results on the role of knowledge brokers regarding the use of evidence in health organizations and public policy.
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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 | 0.027 | 0.118 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.012 |
| Science and technology studies | 0.015 | 0.007 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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 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".