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Record W4235099221 · doi:10.31236/osf.io/cqv48

Physical inactivity: A behavioral disorder in the physical therapist’s scope of practice

2019· preprint· en· W4235099221 on OpenAlexaff
Matthieu P. Boisgontier, Maura D. Iversen

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsPhysical healthPhysical activityScope (computer science)PsychologyGold standard (test)Mental healthMedicinePsychiatryPhysical therapyComputer science

Abstract

fetched live from OpenAlex

Physical activity is considered a strategy to improve health. This reasoning implies that physical inactivity is the reference behavior, which is not the case. In health, the gold standard is a state of complete physical, mental and social well-being.1 Physical inactivity involves a higher risk of cardiovascular disease2, hypertension3, diabetes2,4, cancer5, depression6, and obesity7. Moreover, 6 to 10% of all deaths from non-communicable diseases worldwide can be attributed to physical inactivity.8 Therefore, physically active individuals appear to be closer to the health gold standard than inactive individuals. Physical activity – not inactivity – should be the standard reference behavior. In this reversed framework, physical inactivity becomes a clinically significant disturbance in an individual's behavior, which is the definition of a behavioral disorder.9 Therefore, physical inactivity should be treated as such.

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)0.001
metaresearch head score (Gemma)0.009
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: Other · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.065
GPT teacher head0.421
Teacher spread0.355 · 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
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

Citations2
Published2019
Admission routes1
Has abstractyes

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