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Record W1981966100 · doi:10.1177/1069072713487500

An Analysis of Work Engagement Among Workers With Mental Disorders Recently Integrated to Work

2013· article· en· W1981966100 on OpenAlexaff
Patrizia Villotti, Cristian Balducci, Sara Zaniboni, Marc Corbière, Franco Fraccaroli

Bibliographic record

VenueJournal of Career Assessment · 2013
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsCentre for Disability Prevention and RehabilitationUniversité de Sherbrooke
Fundersnot available
KeywordsWork engagementNomological networkPsychologyPath analysis (statistics)Confirmatory factor analysisConstruct validityScale (ratio)Social supportWork (physics)Clinical psychologyPsychometricsSocial psychologyStructural equation modeling

Abstract

fetched live from OpenAlex

The purpose of this study is to determine the validity of the work engagement construct among mentally ill workers and to develop a nomological network delineating the relationship of work engagement with its antecedents, and its consequences in this specific population. Three hundred and ten people with mental disorders employed in Italian social enterprises accepted to take part in this longitudinal study and filled out the Utrecht Work Engagement scale (UWES-9) and questionnaires on severity of symptoms perceived, social support from coworkers and supervisor, and occupational self-efficacy. Individuals who were still eligible at the 12-month follow-up phase of the study completed a questionnaire on future working plans. Confirmatory factor analysis and path analysis were used to validate the UWES-9 and test its nomological network. The results indicated how work engagement, as well as its drivers, impacted important work outcomes, such as to work in the open labor market, in workers with mental disorders.

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)0.001
metaresearch head score (Gemma)0.005
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.386
Teacher spread0.361 · 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

Citations26
Published2013
Admission routes1
Has abstractyes

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