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Record W2170477910 · doi:10.1136/bjsm.2010.080135

Can exercise advice be ‘made to stick’? Combining psychology and technology to improve patient uptake of physical activity prescription

2010· article· en· W2170477910 on OpenAlexaff
Erin M. Macri, Vanessa C Young, Karim M. Khan

Bibliographic record

VenueBritish Journal of Sports Medicine · 2010
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicHealthcare Systems and Technology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedical prescriptionExercise prescriptionMedical educationPhysical activityAlternative medicineEvent (particle physics)Point (geometry)MedicineMedical schoolPsychologyPhysical therapyNursing

Abstract

fetched live from OpenAlex

We are clinicians with various degrees of experience, training in three different countries, and a compelling goal of promoting physical activity. However, the physicians feel (shh!) less than expert at confidently prescribing exercise and the physiotherapist feels concerned that inappropriate prescription could lead to a major adverse event. Professor Garry Jennings (see page 994) provides a practical eight-step programme that will allow you to prescribe exercise with confidence.1 These were not things learnt from a textbook or in medical school: they were learnt at UWE, the University of Wealth of Experience. We would love to see his tips shared during medical school training and in all physiotherapy courses. We particularly appreciated the point that ‘good communication’ …

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.961
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.010
GPT teacher head0.273
Teacher spread0.263 · 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 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

Citations1
Published2010
Admission routes1
Has abstractyes

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