A Longitudinal Person-Centered Perspective on Positive and Negative Affect at Work
Bibliographic record
Abstract
Emilie Sandrina*, Alexandre J. S. Morinb* , Claude Fernetc & Nicolas Gilleta*a Université de Tours; b Concordia University; c Université du Québec à Trois-RivièresEmilie Sandrin is a PhD student at the University of Tours (France) and at the Université du Québec à Trois-Rivières (Canada). Her research interests include work motivation, workaholism, health, attitudes, and behaviors in the work context.Alexandre J. S. Morin, PhD, is Professor in the Department of Psychology of Concordia University (Montreal, Canada) where he chairs the Substantive-Methodological Synergy Research Laboratory. He defines himself as a lifespan developmental psychologist with broad research interests anchored in the exploration of the social and organizational determinants of psychological well-being, self-concept, and commitments at various life stages. His research interests are centered on substantive-methodological synergies aimed at illustrating the usefulness of powerful new statistical methods (including exploratory structural equation models, mixture models, longitudinal models, and multilevel models).Claude Fernet is full professor in organizational behavior at the Université du Québec à Trois-Rivières, Canada. He received his PhD. in psychology from the Université Laval, Canada. Director of the Group for Research on Health and Wellness at Work, his current research interests include job stress, leadership, work motivation, and employee well-being. His work has been published in journals such as Journal of Organizational Behavior, Work & Stress and Journal of Vocational Behavior.Nicolas Gillet is associate professor in work psychology at the University of Tours, France. His current research interests include work motivation, organizational behaviors, and workers' psychological health.* The authors should be considered first authors and contributed equally to the preparation of this article.Supplemental data for this article is available online at https://doi.org/10.1080/00223980.2020.1781033.CONTACT Nicolas Gillet nicolas.gillet@univ-tours.fr Université de Tours, UFR Arts et Sciences Humaines, Département de psychologie, 3 rue des Tanneurs, 37041 Tours Cedex 1, France.AbstractThis research examines how the direction and intensity of employee’s positive and negative affect at work combine within different profiles, and the relations between these profiles and theoretically-relevant predictors (psychological need satisfaction and supervisor autonomy support) and outcomes (work-family conflict, absenteeism, and turnover intentions). A total sample of 491 firefighters completed our measures initially, and 139 of those completed the same measures again four months later, allowing us to examine the stability of these affect profiles over time. Latent profile analyses and latent transition analyses revealed five identical profiles across the two measurements occasions: (1) Low Negative Affect Facilitators; (2) Moderately Low Positive Affect Incapacitators; (3) High Positive Affect Facilitators; (4) Very Low Positive Affect Incapacitators; and (5) Normative. Membership into Profiles 3, 4, and 5 was very stable over time. In contrast, Profiles 1 and 2 were associated with a highly unstable membership over time. The highest levels of work-family conflict, absenteeism, and turnover intentions were associated with the Very Low Positive Affect Incapacitators. In contrast, the lowest levels of turnover intentions were associated with the Low Negative Affect Facilitators and High Positive Affect Facilitators.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".