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Record W4410247238 · doi:10.1016/j.jvb.2025.104138

Newcomer psychological health profiles: A Latent Transition Analysis

2025· article· en· W4410247238 on OpenAlexafffund
Simon A. Houle, Ho Leung Ng, Joon Lee, Alexandre J. S. Morin

Bibliographic record

VenueJournal of Vocational Behavior · 2025
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsUniversité du Québec à Trois-RivièresConcordia University
FundersSocial Sciences and Humanities Research CouncilSocial Sciences and Humanities Research Council of CanadaFonds de Recherche du Québec-Société et CultureMinistère de la Défense Nationale
KeywordsPsychologyTransition (genetics)Social psychologyApplied psychologyClinical psychology

Abstract

fetched live from OpenAlex

Rather than focusing on performance as the ultimate outcome in organizational and vocational research, it has been argued that psychological health represents a far more relevant outcome given its pervasive impact on employee's lives, including their work performance. This longitudinal study examined the various combinations of work and non-work psychological health indicators observed among distinct profiles of newly hired employees ( N = 604; M age = 42.34; 53.4 % males). This study also assessed the stability and generalizability of employee profiles and profile membership over a six-month period. Latent profile analysis revealed six stable newcomer profiles: Apathetic, Detrimental, Normative-Comfortable , Optimal, Workaholic, and Distressed. Newcomer socialization, particularly in relation to the organization and workgroup functioning, were linked to membership into profiles characterized by more favourable psychological states. The profiles were also related to turnover intention, performance, and physical symptoms, highlighting the connection between negative psychological states and adverse outcomes at the organizational and individual levels.

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)0.004
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 score0.007
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.112
GPT teacher head0.517
Teacher spread0.405 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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