Exploring the impact of decentralized leadership on knowledge sharing and work hindrance networks in healthcare teams
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper adopts an explanatory sequential mixed method design to explore the impact of decentralized (vs. centralized) leadership on cross-functional teams' resource exchanges at a long-term care facility in Canada. In the quantitative phase, social network analyses were used to examine the direct and moderated effects (via leader–follower relationship quality; LMX) of the presence of formal decentralized leaders on: (1) knowledge sharing, and (2) work hindrance networks within cross-functional healthcare teams. In the qualitative phase, team members were interviewed regarding the impact of their decentralized leaders. Collectively, the findings suggest that the presence of a decentralized leader may enhance knowledge sharing and safeguard against work hindrance behaviors in cross-functional healthcare teams. However, these effects are contingent on the situation (e.g., LMX quality and status-based hierarchies). Implications for research and healthcare practice are discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it