Hybrid work design profiles: Antecedents and well-being outcomes
Bibliographic record
Abstract
Hybrid work is fast emerging as the future of work. Yet, it is not clear how key work design characteristics that are salient in hybrid work, namely scheduling autonomy, social support, workload, and close monitoring, are experienced in the home compared to the workplace for hybrid workers, and how these work characteristics combine holistically to influence well-being. We adopted a novel approach and measured work characteristics as experienced at home and, separately, as experienced at the workplace. For a sample of hybrid workers ( n = 386), latent profile analysis revealed four profiles of work design characteristics. Two profiles had similar work characteristics at home and the workplace. One of these profiles, labelled ‘active, low monitoring’, had very positive work characteristics across both locations, and was associated with the highest flourishing and mental health. The other profile, labelled ‘passive, high monitoring’, had very poor work design across both locations, and was associated with the lowest flourishing and mental health. The other two profiles diverged in work characteristic levels across locations. One profile, labelled ‘high strain, high monitoring’ had poor work design that was worse in the workplace, and one profile, labelled ‘low strain, low monitoring’, had better work design that was better at the workplace. Employees with more influence over their work location, and those with high organisational support, were likely to be in the most positive profile (active, low monitoring), suggesting these are important factors for creating positive work design irrespective of location. • In a study of hybrid workers, four hybrid work design profiles emerged. • For some people, work quality varies between home and workplace. • Those in a high strain, a high monitoring profile had better work quality at home. • Those in a low strain, a low monitoring profile had better work quality at the workplace. • Managers should consider monitoring as a job demand when managing remote workers.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".