A qualitative descriptive study of the role of nurse, allied health and physician middle managers who function as knowledge brokers in hospitals
Bibliographic record
Abstract
BACKGROUND: Knowledge brokers (KB) are increasingly being employed in health care to implement evidence-based practice and improve quality of care. Middle managers (MMs) may play a KB role in the implementation of an innovative or evidence-based practice in hospitals. However, how MMs' broker knowledge in hospitals and their impact on practice has not been adequately studied. AIM: To describe the role that MMs play in brokering knowledge in hospitals and their impact. METHOD: A qualitative descriptive study was conducted to generate a detailed description of MM experiences as KBs in hospitals. Data were collected using semi-structured telephone interviews with MMs in Ontario, Canada. Participants were purposively sampled to ensure variation in MM characteristics and a diverse representation of perspectives. Data were collected and analyzed concurrently using an inductive constant comparative approach. RESULTS: Twenty-one MMs from teaching and non-teaching hospitals participated. MMs described 10 roles and activities they enacted in hospitals that aligned with published KB roles. We found differences across professional groups and hospital type. Teaching status emerged as a potential factor relating to how MM KBs were able to function within hospitals. MMs reported enhanced patient, provider, and organizational outcomes. LINKING EVIDENCE TO ACTION: Middle managers may play an important KB role in the implementation of evidence-based practice in hospitals. An improved understanding of the KB roles that MMs play may be important in boosting evidence base practice in health care to ultimately improve quality of care. Administrators need a better understanding of the current KB roles and activities MMs enact as this may lead to more organizational structures to support MM KBs in 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 | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".