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Record W4388707148 · doi:10.52678/001c.89008

An Exploratory Study of Employee Engagement in Human Service Agencies

2023· article· en· W4388707148 on OpenAlexaff
Nina Esaki, Xiaofang Liu, Rosemary Vito

Bibliographic record

VenueJournal of Human Services · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsWestern University
Fundersnot available
KeywordsEmployee engagementExploratory researchEmployee researchEmployee resource groupsHuman resource managementAntecedent (behavioral psychology)Human resourcesHuman servicesBusinessService (business)Sample (material)Public relationsJob satisfactionSupervisorPsychologyMarketingManagementSocial psychologySociologyPolitical science

Abstract

fetched live from OpenAlex

Given research suggesting that engaged employees demonstrate greater workplace performance, employee engagement has been one of the highest trending topics in the management and human resource literature over the past 20 years; yet there is minimal empirical research focused specifically on employee engagement in nonprofit organizations. The purpose of this study was to explore antecedent factors that contribute to employee engagement in the human services sector, using a convenience sample of staff in human service agencies in the New York City region. Organizational trust, satisfaction with supervisor, and coworker support were significant predictors of employee engagement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.007
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0070.002
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.098
GPT teacher head0.390
Teacher spread0.291 · 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 source (direct Gemma or distilled Codex), 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

Citations5
Published2023
Admission routes1
Has abstractyes

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