Leaders' individualized consideration, team commitment and patient loyalty: The role of social and task‐related contexts
Bibliographic record
Abstract
Abstract Drawing from transformational leadership (TFL) theory and research on contextual leadership, we examined a conditional process model of leadership in nursing teams to predict patient loyalty. Using TFL's individualized consideration dimension as a salient facet of the construct in care services, we first posited that nurses' team affective commitment would partially mediate the impact of nurse leadership. We further conceptualized nurse–physician collaboration, organizational formalization and task feedback as discrete contexts that may curb the influence of head nurses' individualized consideration. In a three‐wave, multisource and multilevel study, we surveyed 654 nurses and 1770 patients from 91 hospital units. We found that team‐level head nurses' individualized consideration positively and partially related to patient loyalty through nurses' team commitment and that higher levels of nurse–physician collaboration, organizational formalization and task feedback were associated with reduced influence of individualized consideration on team commitment and patient loyalty. We discuss the implications of these findings for advancing theory and research on TFL and contextual factors of leadership.
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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 | 0.002 | 0.010 |
| 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 | 0.000 |
| Open science | 0.000 | 0.002 |
| 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".