P-413 THE ROLE OF PSYCHOLOGICAL NEEDS SATISFACTION AND APPRAISAL IN THE JOB DEMANDS-RESOURCES MODEL OF BURNOUT AND ENGAGEMENT
Bibliographic record
Abstract
Abstract Introduction Workplace mental health issues, and particularly burnout (Jackson & Maslach, 1982) are increasing, with detrimental consequences for all. The Job Demands-Resources Model (JD-R; Demerouti, et al., 2001) is one of the dominant burnout models, but there have been inconsistent findings, with demands sometimes leading to positive outcomes. Drawing on JD-R research, the challenge-hindrance framework (Lepine et al., 2005) and self-determination theory (Deci et al, 2001), this study investigates how appraisals and psychological needs satisfaction affect relationships between demands, and burnout and engagement. Methods A time-lagged online survey with 2 measurement occasions was conducted with 160 employees across diverse locations and occupations (51.9% women, mean age 30). Data were analyzed using multiple regression and PROCESS. Results Hindrance appraisals strengthened the positive relationship between demands and burnout, and demands were negatively related to engagement when hindrance appraisal was high, but positively contributed to it when hindrance appraisal was low. Challenge appraisals weakened a positive relationship between demands and engagement, as engagement remained high regardless of the level of demands. Similarly, positive relationships between demands and engagement were weaker when relatedness and autonomy needs satisfaction were high. Discussion Findings suggest hindrance appraisals enhance the negative impact of demands on burnout and reverse potential benefits for engagement. Interestingly, challenge appraisals and satisfied needs weakened relationships between demands and engagement, possibly because when employees perceive their work to be challenging and psychological needs to be satisfied, greater demands is less impactful. Conclusion This study highlights the relevance and complex functioning of appraisals and need satisfaction in the burnout/engagement process.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".