Exploring the Networking of Academic Health Science Leaders: How and Why Do They Do It?
Bibliographic record
Abstract
PURPOSE: Networking is essential to leadership effectiveness in the business context. Yet little is known about leadership networking within the academic health science context. If we are going to train academic leaders, we must first understand the relational, network-based activities of their work. The purpose of this study was to explore how academic health science leaders engage in networking activities in the academic health science context. METHOD: A constructivist grounded theory approach guided our study. The authors interviewed 24 academic health science leaders who were enrolled in the New and Evolving Academic Leadership program at the University of Toronto and used social network mapping as an elicitation method. Interviews, which were conducted between September 2014 and June 2015, explored participants' networks and networking activities. Constant comparative analysis was used to analyze the interviews, with attention paid to identifying key networking activities. RESULTS: Academic health science leaders were found to engage in 4 types of networking activities: role bound, project based, goal/vision informed, and opportunity driven. These 4 types were influenced by participants' conception of their role and their perceived leadership work context, which in turn influenced their sense of agency. CONCLUSIONS: The networking activities identified in this study of academic health science leaders resonate with effective networking activities found in other fields. The findings highlight that these activities can be facilitated by focusing on leaders' perceptions about role and work context. Leadership development should thus attend to these perceptions to encourage effective networking skills.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".