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Record W2983302571 · doi:10.7575/aiac.ijkss.v.7n.4p.22

Physical Activity Counseling in Kinesiology Curricula: What is Offered in Ontario?

2019· article· en· W2983302571 on OpenAlexaffabout
Philip M. Wilson, Caitlin Kelly, Diane E. Mack, Colin M. Wierts

Bibliographic record

VenueInternational Journal of Kinesiology and Sports Science · 2019
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British ColumbiaBrock University
Fundersnot available
KeywordsKinesiologyCurriculumMedical educationPsychomotor learningPsychologyHigher educationPedagogyMedicinePhysical therapyPolitical scienceNeuroscience

Abstract

fetched live from OpenAlex

Background: Physical activity counseling (PAC) is a viable approach for individualizing behavior change yet it is unclear if training opportunities in this area constitute a portion of the curriculum offered to university students by kinesiology departments. Objectives: The purpose of this study was to describe the availability of courses in PAC within the curricular offered by kinesiology departments at the post-secondary level. Methods: Data were extracted from the 2018-2019 undergraduate calendars published by kinesiology departments from universities in Ontario, Canada. Results: Seventeen of the 22 universities (77.3%) reported a department of kinesiology (or equivalent). Every kinesiology department offered courses in human biomechanics and human psychomotor learning or neuroscience. Less than half (n = 7; 41.2%) of these kinesiology departments offered PAC courses. Conclusions: Overall, this study makes it apparent that university students completing a kinesiology degree may have limited access to formal training opportunities devoted exclusively to PAC in comparison to other knowledge domains (e.g., human biomechanics). Based on these results, it seems reasonable to contend that kinesiology programs may warrant reconfiguring to meet the occupational demands of exercise professionals who use PAC to combat physical inactivity.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.275

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.020
GPT teacher head0.331
Teacher spread0.311 · 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.

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

Citations2
Published2019
Admission routes2
Has abstractyes

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