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Record W4283655393 · doi:10.36834/cmej.73767

Physical activity RX: development and implementation of physical activity counselling and prescription learning objectives for Canadian medical school curriculum

2022· article· en· W4283655393 on OpenAlexaffvenueabout
L Capozzi, Victor Lun, Erin M. Shellington, Taniya S. Nagpal, Jennifer R. Tomasone, Catherine A. Gaul, Arielle Roberts, Jonathon R. Fowles

Bibliographic record

VenueCanadian Medical Education Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsAcadia UniversityQueen's UniversityUniversity of British ColumbiaUniversity of CalgaryUniversity of VictoriaBrock University
Fundersnot available
KeywordsPhysical activityMedical prescriptionCurriculumMedical educationMedicineHumanitiesPsychologyNursingPedagogyPhysical therapy

Abstract

fetched live from OpenAlex

Physical activity is an important component of health and well-being, and is effective in the prevention, management, and treatment of numerous non-communicable chronic diseases. Despite the known health benefits of physical activity in all populations, most Canadians do not meet physical activity recommendations. Physicians play a key role in assessing, counselling, and prescribing physical activity. Unfortunately, many barriers, including the lack of adequate education and training, prevent physicians from promoting this essential health behaviour. To support Canadian medical schools in physical activity curriculum development, a team of researchers, physicians, and exercise physiologists collaborated to develop a key set of learning objectives deemed essential to physician education in physical activity counselling and prescription. This commentary will review the newly developed Canadian Physical Activity Counselling Learning Objectives and give case examples of three Canadian medical schools that have implemented these learning objectives.

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)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.349
Teacher spread0.331 · 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.

Study designOther design
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

Citations11
Published2022
Admission routes3
Has abstractyes

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