THE IMPACT OF WORKING FROM HOME ON PRODUCTIVITY. A STUDY ON THE PANDEMIC PERIOD
Bibliographic record
Abstract
The study on working from home productivity has received much interest from researchers in the recent years. Numerous studies have found working from home to be productive. However, now with the coronavirus pandemic turning many more people into remote workers overnight, is working from home still productive? This is what the current study aims to find, taking into account that the employees have no choice during this pandemic period; they are forced to work from home. Before the pandemic, the employees were consent with working from home and usually they were coming in the working environment at least one day a week, but usually two or three days. Full-time working at home may be different. Moreover, the employees are now forced to work from home without any training or preparation. Most of them have never worked from home before. Even if the employees worked from home occasionally and they are trained for it, yet they are not prepared for such a long period away from their working environment. Working from home still has its advantages: there are no face-to-face meetings, no distractions from co-workers, no annoying managers to boss them around, no wasted time in traffic, no worries about the children’s safety, as they are at home. Besides the advantages, working from home comes also with some disadvantages. The specialists worry about the negative effects, such as an explosion of mental health issues that could also generate physical health problems. The current study focuses on finding the advantages and disadvantages of working from home, but also on ways to make working from home more effectively. For this purpose, a questionnaire was administered to the employees from three private companies in Bihor county. The final results indicate a negative effect of working from home on productivity. We have found the main benefits and challenges and also the ways to improve productivity when working from home.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".