Promoting Workers’ Well-being Through Human Resource Management Practices and Job Crafting
Bibliographic record
Abstract
The COVID19’crisis put more pressure on organizations to care about employees’ well-being and to support them to adapt to their altered work environment. Because many approaches in the field of strategic human resource management have failed to consider seriously employees well-being, there is a need for a sustainable and well-being-oriented approach. This study examines the links between well-being-oriented human resource management (WBHRM), job crafting and well-being at work using two waves sample of nurses working in healthcare organizations in Quebec-Canada (N= 344). Based on the conservation of resource theory and using structural equation methods to analyze data, results show when nurses who perceive the value of WBHRM in context of crises as high, they reinvest more resources over time in form of job crafting by actively crafting more decisions on how to do their work, by reorganizing work to be more effective, by using more technology to improve work process and by developing more their skills and capacities to acquire additional resources such as well-being. The findings reveal that job crafting mediates the link between WBHRM and well-being at work.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".