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Workplace ergonomics problems and solutions: Working from home

2021· preprint· en· W3204356557 on OpenAlexaff
Jian Ai Yeow, Poh Kiat Ng, Wei Yin Lim

Bibliographic record

VenueF1000Research · 2021
Typepreprint
Languageen
FieldSocial Sciences
TopicGender Studies and Social Issues
Canadian institutionsUniversité de Moncton
Fundersnot available
KeywordsHuman factors and ergonomicsWork (physics)DeskPsychologyMedicinePoison controlEngineeringEnvironmental healthMechanical engineering

Abstract

fetched live from OpenAlex

Background: Due to the COVID-19 pandemic, in 2020, many employees were required to work from home (WFH). During this WFH period, some employees encountered health issues related to sprains and neck or back pain owing to poor working conditions at home. As the WFH trend may continue over a prolonged period, the underlying causes and solutions to ergonomic issues must be addressed to reduce injuries. This study aims to identify the ergonomic issues encountered when working from home and suggests several solutions to minimise these issues. Methods: A qualitative ethnographic methodology was adopted. This study used focus group discussion and the panellists were among experts from the fields of higher education, healthcare, human resources (HR), and ergonomics patient in Malaysia. The most common ergonomic issues identified were based on diagnoses and observations in previous studies. Results: The panellists agreed on ergonomics issues, comprising the use of unergonomic chairs, incorrect sitting postures, irregular arrangement of key objects, improper reach distances of the laptop/keyboard/mouse, poor desk designs, footrest absence, distortion/noise, poor lighting, and poor work environment. Over time, WFH ergonomics issues may lead to burnout, carpal tunnel syndrome or other cumulative trauma disorders, high blood pressure, and stress on the cervical spine and neck. The proposed solutions include a complete WFH ergonomics and wellness checklist for employees and employers, webinar sessions on WFH ergonomics, meet-up sessions with ergonomics or HR experts, workspace rentals for co-workers, implementation of the 20-20-20 rule and job-sharing practices, and the involvement of employers or the government in procuring ergonomic equipment for WFH employees. Conclusions: This is a preliminary study and the researchers are exploring the root causes of WFH ergonomics issues and proposed solutions. While previous studies have examined workplace ergonomics, this study focuses on WFH ergonomic issues and solutions during the ongoing pandemic.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0060.002
Scholarly communication0.0040.004
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.001

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.

Opus teacher head0.201
GPT teacher head0.377
Teacher spread0.176 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2021
Admission routes1
Has abstractyes

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