Failing to Serve the Frontlines: Nurses’ Perceptions of HRM in the Midst of a Crisis
Bibliographic record
Abstract
In this paper, we investigate nurses’ perceptions of the role of HRM in addressing their needs as frontline employees, and how those perceptions changed during the height of the COVID-19 pandemic. By integrating literature from the areas of frontline service employee management, HRM, and crisis management into our longitudinal, qualitative study, we identify three primary roles of HRM that the nurses felt were critical to supporting their wellbeing and sustained delivery of quality patient care during the crisis, but were lacking from HRM in their hospitals, namely communication, planning and addressing emotional demands. Importantly, our findings also emphasize the dynamic nature of employee perceptions of HRM, particularly as a crisis compounds existing job demands on the frontlines, necessitating a more responsive form of HRM. Ultimately, we propose the need for a more reflexive, differentiated approach to HRM, built to meet the unique needs of frontline service employees during a crisis.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".