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Record W2522863885 · doi:10.1037/ocp0000052

Consequences of work group manpower and expertise understaffing: A multilevel approach.

2016· article· en· W2522863885 on OpenAlexaff
Cristina K. Hudson, Winny Shen

Bibliographic record

VenueJournal of Occupational Health Psychology · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicJob Satisfaction and Organizational Behavior
Canadian institutionsUniversity of Waterloo
FundersUniversity of South Florida
KeywordsConceptualizationAmbiguityWorkloadPsychologyWork (physics)PsycINFOWorking groupSocial psychologyApplied psychologyManagementComputer scienceMEDLINEPolitical scienceEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Complaints of chronic understaffing in organizations have become common among workers as employers face increasing pressures to do more with less. Unfortunately, despite its prevalence, there is currently limited research in the literature regarding the nature of workplace understaffing and its consequences. Taking a multilevel approach, this study introduces a new multidimensional conceptualization of subjective work group understaffing, comprising of manpower and expertise understaffing, and examines both its performance and well-being consequences for individual workers (Study 1) and work groups (Study 2). Results show that the relationship between work group understaffing and individual and work group emotional exhaustion is mediated through quantitative workload and role ambiguity for both levels of analysis. Work group understaffing was also related to individual job performance, but not group performance, and this relationship was mediated by role ambiguity. Results were generally similar for the 2 dimensions of understaffing. Implications for theory and research and future research directions are discussed. (PsycINFO Database Record

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 score0.140
Threshold uncertainty score0.281

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.103
GPT teacher head0.375
Teacher spread0.272 · 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

Citations22
Published2016
Admission routes1
Has abstractyes

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