Excessive Change, Emotional Exhaustion and Employee Wellbeing: How Change Impacts Overload
Bibliographic record
Abstract
Frontline workers have experienced important changes in work demands related to the fast pace of technological and socio-economic changes. Excessive change in frontline service organizations has had a detrimental effect on the mental health of workers. The COVID-19 pandemic imposed additional disruptive change on those working in already difficult environments. Previous research done during the pandemic has demonstrated poor mental health in frontline workers however the attribution of negative outcomes to pandemic-related changes is unclear as it is entangled with pre-existing challenges. This study examines data collected from two samples of police officers taken before (N=2590) and after (N=2035) the onset of the COVID-19 pandemic and a panel (N=259) drawn from these samples. Drawing on Job-Demands-Resources theory, we find that poor mental health in frontline workers cannot be attributed solely to the disruptive change of the pandemic. This study provides empirical support for the sequential relationship of job demands predicting negative mental health outcomes over time and for the “loss spiral” of mental health that may be induced by excessive change. For HR managers, the findings illuminate the need to consider the risk of negative near-term effects on mental health even in interventions targeting work demands reductions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".