Engagement of employees in a research organisation: A relational perspective
Bibliographic record
Abstract
Background: Increasing work engagement in a sustainable way remains a challenge despite years of research on the topic. Relationships at work are vital to foster engagement or disengagement. While the relational model by Kahn and Heaphy is conceptually appealing to explain work engagement, it lacks empirical support.Aims: The aims of this study were to investigate the associations among relational factors, psychological conditions (psychological meaningfulness, availability and safety) and work engagement and to test a structural model of work engagement.Setting: A total of 443 individuals in an agricultural research organisation participated in a cross-sectional study.Methods: Four scales that measured relational factors, the Psychological Conditions Scale and the Work Engagement Scale were administered. Latent variable modelling was used to test the measurement and structural models.Results: The results confirmed a structural model in which relational facets of job design contributed to psychological meaningfulness. Emotional exhaustion (inverse) and co-worker relationships contributed to psychological availability. Supervisor relationships contributed to psychological safety. Psychological meaningfulness and psychological availability contributed to work engagement, while emotional exhaustion contributed to disengagement.Conclusion: The relational context is an important target for intervention to affect the psychological conditions which precede work engagement. To promote work engagement, it is vital to focus on psychological meaningfulness, psychological availability and emotional exhaustion.
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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 | 0.006 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".