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Record W2314580193 · doi:10.1177/2150131910380421

Physician Counseling and Longer Term Physical Activity

2010· article· en· W2314580193 on OpenAlexaff
Kevin S. Spink, Kathleen Wilson

Bibliographic record

VenueJournal of Primary Care & Community Health · 2010
Typearticle
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineTelephone counselingPhysical therapyFamily medicineRandomized controlled trialPhysical activityGroup counselingPaceClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

While physician counseling has been suggested as a strategy to promote physical activity, there is insufficient evidence to support its effectiveness at present. The purpose of this study was to examine the effect of brief physician counseling (modified PACE protocol) and telephone follow-ups on changes in the energy expenditure levels of patients over a 20-month period. Forty-five participants completed physical activity questionnaires at both baseline and 20 months. Following brief physician counseling (modified PACE protocol), patients were randomized into a counseling-only group or an enhanced counseling group that included 3 telephone follow-ups. Energy expenditure significantly increased from baseline (1.5 kcal/kg/d [KKD]) to 20 months (2.2 KKD, P < .05) in both groups. Neither the group nor group-by-time interaction was significant (P > .05). In line with the counseling provided by physicians, participants showed an increase in moderate intensity activities and a decrease in light intensity activities (Ps < .001). These findings provide support for the effectiveness of brief physician counseling. However, the additional telephone support did not appear to enhance the physician counseling.

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)0.001
metaresearch head score (Gemma)0.005
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 score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.039
GPT teacher head0.356
Teacher spread0.317 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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