MétaCan
Menu
Back to cohort
Record W4310154888 · doi:10.3390/mps5060094

Physical Movement Habit Formation in Sedentary Office Workers: Protocol Paper

2022· article· en· W4310154888 on OpenAlexaff
Kailas Jenkins, Jena Buchan, Ryan E. Rhodes, Kyra Hamilton

Bibliographic record

VenueMethods and Protocols · 2022
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsHabitIntervention (counseling)Office workersSedentary behaviorPopulationPsychologySedentary lifestyleProtocol (science)Physical activityCued speechTest (biology)Movement (music)Physical therapyApplied psychologyGerontologyMedicineEnvironmental healthSocial psychologyEngineeringPsychiatryOperations managementAlternative medicine

Abstract

fetched live from OpenAlex

Engaging in physical movement has a number of mental and physical health benefits, and yet 45% of Australia's population do not meet the recommended guidelines for physical activity. The current study aims to develop an online habit-based intervention designed to reduce sedentary behavior within the workplace, using environmental cues to instigate simple behavioral changes. Participants in this study will include full time office workers who self-report as having a highly sedentary job and work from either a commercial office, home office, or a mixture of both. Participants will complete a habit-based intervention over a four-week period designed to reduce sedentary behavior by increasing habitual responses to simple physical movement behaviors cued by their environment. Analysis will involve mixed methods ANOVAs to test the efficacy of the intervention. A successful intervention will show a reduction in sedentary behavior as a response to habitual simple physical movement behaviors.

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)0.024
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Protocol · Consensus signal: Protocol
Teacher disagreement score0.091
Threshold uncertainty score0.303

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.024
Meta-epidemiology (narrow)0.0030.003
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.002
Science and technology studies0.0050.002
Scholarly communication0.0030.002
Open science0.0030.002
Research integrity0.0050.006
Insufficient payload (model declined to judge)0.0910.021

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 head0.123
GPT teacher head0.543
Teacher spread0.420 · 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 designNot applicable
Domainnot available
GenreProtocol

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

Citations1
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueMethods and ProtocolsSame topicBehavioral Health and InterventionsFrench-language works237,207