Boundary Spanning by Nurse Managers: Effects of Managers’ Characteristics and Scope of Responsibility on Teamwork
Bibliographic record
Abstract
Increasing role complexity has intensified the work of managers in supporting healthcare teams. This study examined the influence of front-line managers' characteristics and scope of responsibility on teamwork. Scope of responsibility considers the breadth of the manager's role. A descriptive, correlational design was used to collect cross-sectional survey and administrative data in four acute care hospitals. A convenience sample of 754 staff completed the Relational Coordination Scale as a measure of teamwork that focuses on the quality of communication and relationships. Nurses (73.9%), allied health professionals (14.7%) and unregulated staff (11.7%) worked in 54 clinical areas, clustered under 30 front-line managers. Data were analyzed using hierarchical linear modelling. Leadership practices, clinical support roles and compressed operational hours had positive effects on teamwork. Numbers of non-direct report staff and areas assigned had negative effects on teamwork. Teamwork did not vary by span, managerial experience, worked hours, occupational diversity or proportion of full-time employees. Large, acute care teaching hospitals can enable managers to foster teamwork by enhancing managers' leadership practices, redesigning the flow or reporting structure for non-direct reports, optimizing managerial hours relative to operational hours, allocating clinical support roles, reducing number of areas assigned and, potentially, introducing co-manager models.
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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 | 0.005 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".