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Record W4214771545 · doi:10.1136/bjsports-2020-102976

Infographic. One small step for man, one giant leap for men’s health: a meta-analysis of behaviour change interventions to increase men’s physical activity

2020· review· en· W4214771545 on OpenAlexaff
Paul Sharp, John C. Spence, Joan L. Bottorff, John L. Oliffe, Kate Hunt, Mathew Vis‐Dunbar, Adam Virgile, Cristina M. Caperchione

Bibliographic record

VenueBritish Journal of Sports Medicine · 2020
Typereview
Languageen
FieldMedicine
TopicPhysical Activity and Health
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaUniversity of Alberta
Fundersnot available
KeywordsMeta-analysisPsychological interventionPhysical activityInfographicPsychologyGerontologyMedicinePhysical therapyComputer sciencePsychiatry

Abstract

fetched live from OpenAlex

First paragraph: Health promotion programmes focused on improving physical activity have traditionally failed to engage and retain men, resulting in under-represented outcomes and challenges with generalisability. Recent interest and developments in men’s health research have led to an increased number of interventions specifically targeted at engaging and retaining men. In our recent systematic review and meta-analysis, published in the British Journal of Sports Medicine, we aimed to determine the effects of behaviour change interventions on men’s physical activity and to identify potential moderators of intervention effectiveness (eg, theoretical underpinning, gender-tailored, contact frequency). Study findings are summarised below and in the accompanying infographic.

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)0.007
metaresearch head score (Gemma)0.064
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.243
Threshold uncertainty score0.814

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.064
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0060.006
Bibliometrics0.0090.009
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.2430.013

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 head0.389
GPT teacher head0.448
Teacher spread0.059 · 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 designMeta-analysis
Domainnot available
GenreReview

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

Citations3
Published2020
Admission routes1
Has abstractyes

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