How Leaders Lift Us Up and Bring Us Down: Relationship Quality With a Leader, Team Dynamics, and Outcomes During a Crisis
Bibliographic record
Abstract
ABSTRACT We investigate the nature of team members' relationship with their leader, team dynamics, and outcomes during a continuous organisational crisis in a healthcare setting. Leaders ( n = 24) and team members ( n = 150) completed matched surveys at three hospitals. Individuals who felt they had a stronger relationship with their leader than their teammates (i.e., higher on leader membership exchange (LMX) than their team average), performed better, were less likely to want to leave their job, and were more confident in their team's ability to succeed (i.e., higher team potency). Teams higher on LMX reported fewer turnover intentions, and were more creative. Both individuals' and team's core self‐evaluations (CSE) were linked to positive outcomes, including higher team potency amongst teams with higher CSE. For weak leaders (i.e., team‐rated low LMX or perceived expertise), individuals' positive CSE were associated with better performance. Implications and future research directions for crisis management are provided.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".