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Record W4391734378 · doi:10.53935/jomw.v2022i2.193

The Impact of Unit Membership, Discipline, and Age on Team Effectiveness

2022· article· en· W4391734378 on OpenAlex
Elizabeth I. Bradley, Benjamin A. Hudson, Scott M. Welch

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Management World · 2022
Typearticle
Languageen
FieldComputer Science
TopicSoftware Engineering Techniques and Practices
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsUnit (ring theory)PsychologySociologyMathematics education

Abstract

fetched live from OpenAlex

The research on the idea of teams as dynamic systems that transcend boundaries is increasingly attempting to better understand how changes in team members (i.e., team members joining and/or leaving) shape organizational team performance and operation. The purpose of this study was to determine the impact that unit membership, discipline and various demographic characteristics such as age and gender have on levels of stress, coping, self-esteem and estimates of team effectiveness. Stress from patient behaviors was influenced by team and the psychiatric and Alzheimer's units had the highest levels and the lowest levels were in the geriatric and nursing home units. The amount of emotion related coping was significantly affected by age and the youngest participants had the greatest amount. Unit membership and discipline have a significant impact on the level of stress and coping.

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.

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)0.002
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 score0.685
Threshold uncertainty score0.179

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.000
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 head0.019
GPT teacher head0.306
Teacher spread0.288 · 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