The roles, activities and impacts of middle managers who function as knowledge brokers to improve care delivery and outcomes in healthcare organizations: a critical interpretive synthesis
Bibliographic record
Abstract
BACKGROUND: Middle Managers (MMs) are thought to play a pivotal role as knowledge brokers (KBs) in healthcare organizations. However, the role of MMs who function as KBs (MM KBs) in health care is under-studied. Research is needed that contributes to our understanding of how MMs broker knowledge in health care and what factors influence their KB efforts. METHODS: We used a critical interpretive synthesis (CIS) approach to review both qualitative and quantitative studies to develop an organizing framework of how MMs enact the KB role in health care. We used compass questions to create a search strategy and electronic searches were conducted in MEDLINE, CINAHL, Social Sciences Abstracts, ABI/INFORM, EMBASE, PubMed, PsycINFO, ERIC and the Cochrane Library. Searching, sampling, and data analysis was an iterative process, using constant comparison, to synthesize the results. RESULTS: We included 41 articles (38 empirical studies and 3 conceptual papers) that met the eligibility criteria. No existing review was found on this topic. A synthesis of the studies revealed 12 MM KB roles and 63 associated activities beyond existing roles hypothesized by extant theory, and we elaborate on two MM KB roles: 1) convincing others of the need for, and benefit of an innovation or evidence-based practice; and 2) functioning as a strategic influencer. We identified organizational and individual factors that may influence the efforts of MM KBs in healthcare organizations. Additionally, we found that the MM KB role was associated with enhanced provider knowledge, and skills, as well as improved organizational outcomes. CONCLUSION: Our findings suggest that MMs do enact KB roles in healthcare settings to implement innovations and practice change. Our organizing framework offers a novel conceptualization of MM KBs that advances understanding of the emerging KB role that MMs play in healthcare organizations. In addition to roles, this study contributes to the extant literature by revealing factors that may influence the efforts and impacts of MM KBs in healthcare organizations. Future studies are required to refine and strengthen this framework. TRIAL REGISTRATION: A protocol for this review was not registered.
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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 | 0.171 | 0.289 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.035 | 0.019 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".