Hypersensitivity to Social Rejection and Perceived Stress as Mediators between Attachment Anxiety and Future Burnout: A Prospective Analysis
Bibliographic record
Abstract
Drawing on Sociometer Theory, the current study examined whether the tendency to focus on and worry about social rejection at the workplace can predict stress and burnout. Data were collected at two time points from 231 hotel employees. Prospective‐longitudinal design, structural equation modeling analyses revealed that participants' hypersensitivity to social rejection at the workplace predicted an increase in stress and in burnout across the 1 month of participation. Furthermore, the findings revealed that hypersensitivity to social rejection fully mediated the link between attachment anxiety and future stress and that hypersensitivity to social rejection and stress fully mediated the link between attachment anxiety and future burnout. Approximately 64 per cent of the variance in future burnout was explained by these variables. The results demonstrate the significant role social evaluative stressors play in the development of stress responses at the workplace. S’appuyant sur la sociometer theory, la présente étude examine si la tendance à se préoccuper et s’inquiéter du rejet social sur le lieu de travail peut prédire le stress et l’épuisement. Les données ont été collectées par deux fois auprès de 231 employés d’hôtellerie. Le traitement des études longitudinales par des analyses de modélisation par équations structurelles révèle que l’hypersensibilité des sujets au rejet sur le lieu de travail contribue à une augmentation du stress et de l’épuisement au cours du mois de participation. Les conclusions soulignent que l’hypersensibilité au rejet social est totalement influencée par le lien entre anxiété, attachement et stress futur et que l’hypersensibilité au rejet social et au stress est totalement influencée par le lien entre anxiété, attachement et épuisement à venir. Approximativement 64% de la variance de l’épuisement à venir est expliqué par ces variables. Les résultats montrent le rôle significatif joué par des sources de stress liées au jugement social d’autrui dans le développement des réponses de stress sur le lieu de travail.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".