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Record W2415812952 · doi:10.1177/1715163515579221

Do wearable activity trackers have a place in pharmacies?

2015· article· en· W2415812952 on OpenAlexaffvenue
Melissa Li, Kelly Grindrod

Bibliographic record

VenueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2015
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsWearable computerActivity trackerBitTorrent trackerPharmacyComputer scienceMedicineEmbedded systemComputer visionNursing

Abstract

fetched live from OpenAlex

Our bodies are built for movement, no matter our age.Yet most older adults are sedentary for at least 8.5 hours a day. 1 Sedentary behaviour refers to the activities we do sitting or lying down where little energy is expended, such as television watching, computer work and driving. 2 By comparison, physical activity guidelines recommend that all adults, including those older than 65 years, should get at least 150 minutes per week of moderate-intensity aerobic exercise in periods of at least 10 minutes at a time. 3,4We should also do muscle and strength activities at least 2 days per week. 5ogether, physical inactivity and sedentary behaviour increase the risk of diabetes, obesity, cardiovascular disease, breast and colon cancer, premature death and many other chronic diseases. 2,6,7For someone who is inactive, the weekly goal of 150 minutes of physical activity can be daunting if they do not have an effective strategy to becoming more active.It is even more difficult for older adults who have limited mobility, ailing health, poor access to services or a perception that physical activity takes too much time. 8

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)0.011
metaresearch head score (Gemma)0.081
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.312
Threshold uncertainty score0.620

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.081
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.006
Science and technology studies0.0040.002
Scholarly communication0.0070.008
Open science0.0030.002
Research integrity0.0050.003
Insufficient payload (model declined to judge)0.0490.008

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 head0.112
GPT teacher head0.341
Teacher spread0.230 · 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
GenreCommentary

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

Citations8
Published2015
Admission routes2
Has abstractyes

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Same venueCanadian Pharmacists Journal / Revue des Pharmaciens du CanadaSame topicPhysical Activity and HealthFrench-language works237,207