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Record W2741650581

Exercise is Medicine Canada: Early but important signs of the effectiveness of this national initiative

2015· article· en· W2741650581 on OpenAlexaffabout
Christopher Shields, Jonathon R. Fowles, Myles W. O’Brien, Susan Yungblut, Michelle Fortier, Paul Oh

Bibliographic record

VenueJournal of Exercise, Movement, and Sport · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Sciences Research and Education
Canadian institutionsToronto Rehabilitation InstituteUniversity Health NetworkCanadian Society for Exercise PhysiologyUniversity of OttawaAcadia University
Fundersnot available
KeywordsMedical prescriptionPhysical activityMedicineFamily medicineSession (web analytics)Primary careHealth careMedical educationAlternative medicineFoundation (evidence)Work (physics)Multivariate analysis of variancePsychologyPhysical therapyNursingPolitical science
DOInot available

Abstract

fetched live from OpenAlex

Exercise is Medicine Canada (EIMC) is a national initiative aimed at increasing the number of health care providers (HCPs) assessing, counseling and prescribing physical-activity or exercise as part of routine health care visits. An integral component of this program is a national workshop campaign to educate and train primary HCPs on how to effectively use EIMC materials (i.e. prescription pad). Analyses of the immediate impact of these workshops shows HCPs reported improved confidence in their ability to prescribe physical-activity and view this professional development as valuable to their practice. The purpose of this session is to describe the EIMC initiative, review the initial impacts of the workshop series and present the first wave of follow-up evaluations completed 2-3 months post-workshop. Repeated measures MANOVA revealed HCPs report significant increases in confidence to discuss and prescribe physical-activity and exercise (p Acknowledgments: This work was supported by funding from the Lawson Foundation

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)0.007
metaresearch head score (Gemma)0.001
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 score0.134
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.093
GPT teacher head0.405
Teacher spread0.312 · 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

Citations0
Published2015
Admission routes2
Has abstractyes

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