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Record W1981160888 · doi:10.12927/cjnl.2014.23837

Boundary Spanning by Nurse Managers: Effects of Managers’ Characteristics and Scope of Responsibility on Teamwork

2014· article· en· W1981160888 on OpenAlexafffundvenue
Raquel M. Meyer, Linda O’Brien‐Pallas, Diane Doran, David L. Streiner, Christine Duffield

Bibliographic record

VenueNursing leadership · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity Health NetworkMcMaster UniversityBaycrest Hospital
FundersCanadian Institutes of Health Research
KeywordsTeamworkBoundary spanningScope (computer science)NursingSocial responsibilityPsychologyBusinessPublic relationsPolitical scienceManagementKnowledge managementMedicineComputer scienceEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.469
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.251
Teacher spread0.217 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2014
Admission routes3
Has abstractyes

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