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Record W4414371161 · doi:10.1111/apps.70029

The impact of positive work relationships on proactive behaviors: A multilevel study

2025· article· en· W4414371161 on OpenAlexaff
Jennifer B. Farrell, Patrick C. Flood, Gerard P. Hodgkinson, Steven Kilroy, Wladislaw Rivkin, Karoline Strauss

Bibliographic record

VenueApplied Psychology · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsTrinity College
FundersDublin City University
KeywordsIntrapersonal communicationContext (archaeology)ProactivityMultilevel modelSample (material)Test (biology)Work (physics)Social relation

Abstract

fetched live from OpenAlex

Abstract This paper proposes and then tests a cross‐level model pertaining to the intrapersonal and collective antecedents of work‐related proactive behaviors. The model posits individual‐level positive relational experiences and unit‐level relational coordination as social contextual antecedents of individual‐level proactive behaviors. The effects of these mechanisms are hypothesized to be mediated respectively by individual‐level role breadth self‐efficacy and unit‐level psychological safety climate. To test the proposed model, multi‐source data were collected from a representative sample of 246 staff nurses and their respective unit managers, based in four privately owned hospitals. Supporting evidence for the model enriches understanding of the role of social context in variously enabling and undermining the expression of proactive behaviors on the part of individuals, suitably aligned with the wider needs of key organizational units, in safety‐critical work environments. We discuss the implications of our findings for fostering such behavioral alignment and outline directions for future research.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.087
Threshold uncertainty score0.366

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.032
GPT teacher head0.341
Teacher spread0.309 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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