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Record W1991466679 · doi:10.12927/cjnl.2014.23737

Exploring Managers’ Views on Span of Control: More Than a Headcount

2014· article· en· W1991466679 on OpenAlexaffvenue
Carol Wong, Pat Elliott-Miller, Heather Spence Laschinger, Michael Cuddihy, Raquel M. Meyer, Margaret Keatings, Camille Burnett, Natalie Szudy

Bibliographic record

VenueNursing leadership · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicAccounting and Organizational Management
Canadian institutionsHospital for Sick ChildrenBaycrest HospitalOttawa HospitalChildren's Hospital of Eastern OntarioWestern University
Fundersnot available
KeywordsPsychologyControl (management)BusinessManagementEconomics

Abstract

fetched live from OpenAlex

The purpose of this qualitative study was to explore front-line managers' (FLMs') perceptions of their span of control (SOC) and how they manage it. As part of a larger quantitative study examining relationships between FLMs' SOC and performance outcomes, 10 manager focus groups were conducted by teleconference, involving 48 managers from 14 academic healthcare organizations. Themes and subthemes were identified according to (a) perceptions of the size and scope of SOC; (b) factors influencing the complexity of SOC; (c) supports needed to manage SOC; (d) changing leadership style; and (e) ways of coping with role overload. Participants described system demands as a significant contributor to their work responsibilities and a sense of role overload. About half of managers stated their SOC was unreasonable and that they lacked the necessary supports to manage it. Many managers who described their SOC as reasonable still expressed concerns about internal and external workload pressures that contributed to changing leadership style and role overload. Findings reinforce the importance of organizational strategies to create regular dialogue with FLMs regarding the size, complexity and appropriateness of current spans and to provide the resource supports necessary to ensure they can manage their SOC effectively.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.661
Threshold uncertainty score0.989

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.291
GPT teacher head0.262
Teacher spread0.029 · 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

Citations17
Published2014
Admission routes2
Has abstractyes

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