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Record W2058265967 · doi:10.2114/jpa.20.215

Adaptation to Night Shifts and Synchronisation Processes of Night Workers

2001· review· en· W2058265967 on OpenAlexaff
Rabiul Ahasan, John Lewko, Donna Campbell, Alan W. Salmoni

Bibliographic record

VenueJournal of PHYSIOLOGICAL ANTHROPOLOGY and Applied Human Science · 2001
Typereview
Languageen
FieldPsychology
TopicSleep and Work-Related Fatigue
Canadian institutionsCanada Auto WorkersLaurentian University
Fundersnot available
KeywordsCircadian rhythmShift workContext (archaeology)Night workAdaptation (eye)PsychologyChronotypeNeuroscienceHistory

Abstract

fetched live from OpenAlex

Human beings are accustomed to being active and awake during the day, and asleep and rest at night. Since we live in a society which is organised predominantly along daytime activity, therefore working in the night shift may deeply disrupt our social and family life. It is also a well-known fact that night shift causes fatigue and circadian disruption. The basic manifestation of fatigue and circadian rhythm has been linked to health and safety problems, involving decrements in psychophysical and physiological functions, plus subjective complaints. In this context quantitative relationships between shift work and circadian rhythm need to be assessed to explore suitable time schedule, and to minimise sleep depth and fatigue. There is also a great need to discuss circadian disruption, sleepiness and the increasing cost of work related illness among night workers. In this regard, some aspects of fatigue and circadian disruption caused from night shift work are revealed in this paper aiming to increase workers' health, safety and well being as well as productivity. Light/dark cycle and social stimuli issues acting on the circadian timing systems are also explored to solicit opinions and discussion on the controversy of night work. Suggestions are therefore likewise given to enhance workers' adaptation to night shift and synchronization process.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.096
GPT teacher head0.406
Teacher spread0.309 · 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 designOther design
Domainnot available
GenreReview

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

Citations16
Published2001
Admission routes1
Has abstractyes

Explore more

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