Changes in belongingness, meaningful work, and emotional exhaustion among new high‐intensity telecommuters: Insights from pandemic remote workers
Bibliographic record
Abstract
Abstract The COVID‐19 pandemic has thrust millions of workers into high‐intensity telecommuting. While much research has examined the first months of the pandemic, little is known about how workers have responded to this new work arrangement over time. The stressor‐reaction perspective suggests that any strain related to the physical separation from coworkers may persist as long as the stressor is present, while the adaptation perspective implies that individuals adopt new behaviours that help them adjust once the initial shock is over. This research examines the changes in work belongingness, meaningful work, and emotional exhaustion following a shift to high‐intensity telecommuting, between September 2020 and March 2021. We conducted a four‐wave study among an organizational sample of 716 workers who transitioned to high‐intensity telecommuting during the pandemic. Latent growth modelling analyses showed that new high‐intensity telecommuters experienced declines in work belongingness over time, which in turn led to decreased perceptions that their work was meaningful and increased emotional exhaustion, supporting the stress‐reaction perspective. Contrary to theoretical predictions, trajectories were worse for those with a higher initial affective commitment to coworkers. We discuss how our findings can inform scholars and practitioners about the unfolding consequences of a collective shift to high‐intensity telecommuting.
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 | 0.002 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 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".