Bouncing back: HR professionals' experiences during times of disruption
Bibliographic record
Abstract
Purpose This study investigates human resource (HR) professionals' experiences during the COVID-19 pandemic. Design/methodology/approach The study involves in-depth, semi-structured interviews with 37 HR professionals purposefully selected based on their prior involvement in managing pandemic-related challenges. Findings The findings reveal that HR professionals faced intensified organizational demands, leading to expanded job roles, increased workload, a change in pace and emotional pressures. However, participants exhibited resilience by drawing from and creating various job resources to cope with these demands. Our findings also show that despite HR professionals being central to creating workplace support and wellness initiatives, their well-being needs were often overlooked as they prioritized supporting others. Research limitations/implications The study contributes to research on the experiences of HR professionals during the pandemic and to job-demands resources (JD-R) theory by incorporating context-specific demands, resources and coping strategies specific to HR professionals. Practical implications Lessons learned for organizations and HR professionals are discussed in relation to creating conditions of organizational support and resource availability for HR professionals. Originality/value This study extends research on the mental health and well-being of HR professionals during the pandemic by providing a novel lens on linkages between job demands, job resources and self-regulation strategies.
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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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".