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Record W4206918736 · doi:10.3233/wor-210726

Measuring pandemic home-work conditions to determine ergonomic recommendation relevance

2022· article· en· W4206918736 on OpenAlexafffund

Bibliographic record

VenueWork · 2022
Typearticle
Languageen
FieldPsychology
TopicErgonomics and Musculoskeletal Disorders
Canadian institutionsUniversité de Moncton
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsPandemicRelevance (law)Coronavirus disease 2019 (COVID-19)Human factors and ergonomicsHealth careSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)

Abstract

fetched live from OpenAlex

BACKGROUND: Computer-intensive office work associations with health challenges may intensify following COVID-19 pandemic-related changes to home-based office work. OBJECTIVE: To determine working conditions, perception and physical elements affecting health after pivoting to full time home-work. METHODS: An online questionnaire addressed physical, productivity, motivation, and work-practice factors. Photos of the worker in their home-work environment showed side and front-back perspectives. RESULTS: Sixteen questionnaires were received, and 12 respondents supplied photos. Home and office workplace differences varied. Ten felt productivity was affected, most often positively. Four noted increased pain or fatigue intensity, particularly in the eyes, neck / head, lower back, and shoulders. Working posture was not optimal; six didn't use traditional chair-sitting for up to half the day. Forward and backward trunk inclination accounted for at least 10% of the workday for 12 respondents; lateral inclination affected ten and eight had unsupported legs. Fifteen used an adjustable chair, but photos revealed ergonomic recommendations were not consistently followed. Fourteen participants communicated regularly by telephone, eight only for moderate duration and ten using adapted telephone equipment. Half of the ROSA scores were high. CONCLUSIONS: Workers forced into telework during the pandemic experience positive and negative impacts. Postures vary more than in offices, potentially increasing health risk.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.529
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.047
GPT teacher head0.294
Teacher spread0.246 · 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 teacher head, not a consensus.

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

Citations10
Published2022
Admission routes2
Has abstractyes

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