The German Version of the Hybrid Work Characteristics Scale
Bibliographic record
Abstract
Abstract. Introduction: To account for fast-paced developments at work, hybrid work characteristics (HWCs) were introduced. To measure them, an English instrument was developed by Xie et al. (2019) . HWCs encompass more than one work characteristics domain such as the task, social, or contextual domain and include boundarylessness, multitasking, the demand for constant learning, and non-work-related interruptions and are associated with employee attitudes and well-being. Objectives: We validated a German translation of the HWC scale. Method: Using employee samples from Germany ( N = 391) and the United Kingdom ( N = 400), we assessed the quality of the German translation. Results: The German version was internally consistent, showed an acceptable model fit, and reached a scalar level of measurement invariance. The HWCs are related to employee attitudes and well-being. Conclusion: We recommend the use of the German translation of the HWC scale, as our results support its reliability and validity.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".