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Record W2098276664 · doi:10.1123/jpah.5.5.675

Participant Experiences in a Workplace Pedometer-Based Physical Activity Program

2008· article· en· W2098276664 on OpenAlexaff
Nicola Lauzon, Catherine B. Chan, Anita M. Myers, Catrine Tudor‐Locke

Bibliographic record

VenueJournal of Physical Activity and Health · 2008
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPedometerConversationPsychological interventionFocus groupPsychologyApplied psychologyPhysical activityMedical educationSocial psychologyPhysical therapyMedicineCommunicationSociology

Abstract

fetched live from OpenAlex

BACKGROUND: Limited process evaluation of pedometer-based interventions has been reported. METHODS: Feedback via focus groups (n=38) and exit questionnaires (n=68) was used to examine participants' experiences in a group-based, pedometer-based physical activity (PA) program delivered in the workplace. RESULTS: The pedometer was described as a useful tool for increasing awareness of PA, providing motivation and visual feedback, and encouraging conversation and support among participants and others such as family and friends. Group meetings provided motivation and social support, as did participation by coworkers. Self-selected goals, self-selected PA strategies, and recording of steps/d were also important. CONCLUSIONS: Given the importance of social support as a mediating variable in changing PA behavior, future pedometer-based programs might benefit from including a group-based component.

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)0.004
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.199
GPT teacher head0.438
Teacher spread0.239 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations48
Published2008
Admission routes1
Has abstractyes

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