Working where we want: The role of work arrangement fit in work-related and personal wellbeing
Bibliographic record
Abstract
Abstract As hybrid work arrangements have become more prevalent in the wake of the COVID-19 pandemic, the alignment between jobs and workers has also evolved, arguably in ways that research has yet to fully capture. We build on the theoretical foundation of person-environment fit – and person-job fit specifically – to investigate how employees’ work arrangements and their perceived fit with their work arrangements influence important personal (e.g., work-life balance, stress) and work-related (e.g., organizational commitment, engagement) outcomes. Quantitative evidence from a survey of 427 hybrid workers supports the idea that the extent to which an individual’s desires, needs, and values align with their work arrangement plays an important role in their personal and work-related well-being. We advocate for expanding the conceptualization of person-job and person-environment fit models to incorporate work arrangements and provide recommendations for research and practice.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".