Perceived Support Profiles in the Workplace: A Longitudinal Perspective
Bibliographic record
Abstract
This research examines how employee’s perceptions of three sources of support in the workplace (i.e., organization, supervisor, and colleagues) combine within specific profiles and the nature of the relations between these profiles and indicators of employees’ psychological health (i.e., stress, sleep problems, psychosomatic strains, and depression). Furthermore, this research examines the within-sample and within-person stability of the identified support profiles over the course of an 8-month time interval. Latent profile and latent transition analyses conducted on a sample of 729 workers indicated six identical profiles across the two measurement occasions: 1, moderately supported; 2, weakly supported; 3, isolated; 4, well-supported; 5, supervisor supported; and 6, highly supported. Profile membership was very stable over time for most profiles, with the exception of the isolated profile which was only moderately stable. Furthermore, the isolated and supervisor-supported profiles presented the lowest levels of psychological health, while the well-supported and moderately supported profiles presented the highest levels of psychological health. Of particular interest, results suggested that some risks might be associated with the highly supported profile, although this result could be a simple reflection of the women-dominant composition of this profile. This research has implications for theory and practice, which will be discussed in the article.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".