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Record W2793862064 · doi:10.1002/14651858.cd012932

Interactive social media interventions for health behaviour change, health outcomes, and health equity in the adult population

2018· article· en· W2793862064 on OpenAlexaff
Vivian Welch, Jennifer Petkovic, Rosiane Simeon, Justin Presseau, Diane Gagnon, Alomgir Hossain, Jordi Pardo Pardo, Kevin Pottie, Tamara Rader, Alexandra Sokolovski, Manosila Yoganathan, Peter Tugwell, Marie DesMeules

Bibliographic record

VenueCochrane Database of Systematic Reviews · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsPublic Health Agency of CanadaOttawa HospitalCanadian Agency for Drugs and Technologies in HealthUniversity of OttawaBruyère
Fundersnot available
KeywordsPsychological interventionPopulation healthSocial mediaBehavior changeHealth equityPsychologyPopulationBehaviour changeHealth promotionEquity (law)Social determinants of healthBehavior change methodsMedicinePublic healthEnvironmental healthSocial psychologyNursingComputer science

Abstract

fetched live from OpenAlex

This is a protocol for a Cochrane Review (Intervention). The objectives are as follows: The primary objective of this review is to assess the effects of interactive social media interventions aiming to change health behaviour for adults on health behaviours, physical and psychological health outcomes, and any reported adverse effects. The secondary objectives are: to assess the effects of interactive social media interventions that aim to change health behaviour across population subgroups (defined using PROGRESS‐Plus) to assess effects on health equity; to use a validated taxonomy of behaviour change techniques to determine whether social media interventions with specific behaviour change techniques (BCTs) (or BCT combinations) are more effective; to explore heterogeneity of effects to identify other reasons for differences in effects.

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)0.037
metaresearch head score (Gemma)0.093
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.066
Threshold uncertainty score0.220

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0370.093
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0090.012
Bibliometrics0.0090.007
Science and technology studies0.0020.001
Scholarly communication0.0050.004
Open science0.0030.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0660.005

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 head0.328
GPT teacher head0.547
Teacher spread0.220 · 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 designSystematic review
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

Citations50
Published2018
Admission routes1
Has abstractyes

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Same venueCochrane Database of Systematic ReviewsSame topicImpact of Technology on AdolescentsFrench-language works237,207