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Record W6926771000 · doi:10.25316/ir-11810

Active offices: Changing workplace culture by "breaking up the day"

2016· other· en· W6926771000 on OpenAlexaboutno aff

Bibliographic record

VenueVIUSpace (Vancouver Island University Library) · 2016
Typeother
Languageen
FieldMedicine
TopicMicrobial Natural Products and Biosynthesis
Canadian institutionsnot available
Fundersnot available
KeywordsAbsenteeismRecreationPhysical activitySedentary behaviorBehaviour changeProductivityOffice workersSedentary lifestyle

Abstract

fetched live from OpenAlex

The average adult is sedentary between 55 and 71% (9 to 11 hours) of their waking day, many of these in the workplace. Recent research into healthy lifestyles has shifted from measuring time spent in physical activity, to time spent in sedentary behaviours. Independent of regular physical activity, sedentary behaviour contributes to major negative health outcomes, namely obesity, diabetes, cardiovascular disease, cancer, and depression, in addition to reduced workplace productivity and increased absenteeism due to illness. In collaboration with Vivo for Healthier Generations, a community recreation centre in Calgary, Alberta, we performed a pilot project that aimed to reduce sedentary behaviour through feasible and sustainable changes in workplace practice. With the support of their employer, volunteers had their offices retrofitted with sit-stand desks and anti-fatigue mats for a six-month workplace intervention. To complement the workstations, participants were offered motivational support and created individual action plans to personalize their movement goals. Participants in the study were provided workshops, newsletters, and other positive social prompts designed to embed standing and walking into a daily office routine. A mixed-methods approach was used in this six-month pilot study to fully explore the objective measures and the story of the participants.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.005
GPT teacher head0.189
Teacher spread0.185 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2016
Admission routes1
Has abstractyes

Explore more

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