Toward a new model of human resource management practices: construction and validation of the High Wellbeing and Performance Work System Scale
Bibliographic record
Abstract
Introduction: The integrated mutual gains model suggests five provisional sets of human resource management (HRM) practices that should benefit both employees and organizations and, as such, be explicitly designed to have a positive impact on wellbeing, which, in turn, can affect performance. Methods: An extensive review of the literature on scales that used a high-performance work system to assess HRM practices, as well as an extraction of items related to the theoretical dimensions of the integrated mutual gains model, were performed. Based on these preliminary steps, an initial scale with the 66 items found most relevant in the literature was developed and assessed regarding its factorial structure, internal consistency, and reliability over a two-week period. Results: Exploratory factorial analysis following test -retest resulted in a 42-item scale for measuring 11 HRM practices. Confirmatory factor analyses resulted in a 36-item instrument for measuring 10 HRM practices and showed adequate validity and reliability. Discussion: Even though the five provisional sets of practices were not validated, the practices that emerged from them were assembled into alternative sets of practices. These sets of practices reflect HRM activities that are considered conducive to employees' wellbeing and, consequently, their job performance. Consequently, the "High Wellbeing and Performance Work System Scale" was created. Nonetheless, future research is necessary to evaluate the predictive capacity of this new scale.
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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 | 0.012 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".