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Record W4200490000 · doi:10.1080/19317611.2021.2000087

Sociodemographic and Psychological Predictors of Seeking Health Information Online among GB2M in Ontario: Findings from the #iCruise Project

2021· article· en· W4200490000 on OpenAlexafffundabout
David J. Brennan, Maya Kesler, Nathan J. Lachowsky, Adam Davies, Georgi Georgievski, Barry D. Adam, David Collict, Trevor Hart, Travis Salway, Dane Griffiths

Bibliographic record

VenueInternational Journal of Sexual Health · 2021
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsSimon Fraser UniversityUniversity of WindsorPublic Health OntarioUniversity of GuelphUniversity of VictoriaToronto Metropolitan UniversityUniversity of Toronto
FundersMichael Smith Health Research BCCanadian Institutes of Health ResearchOntario HIV Treatment NetworkCanadian Foundation for AIDS Research
KeywordsOutreachInformation seekingInformation seeking behaviorQueerDisseminationPsychologyReproductive healthHealth informationSocial psychologyHealth careDemographySociologyPolitical scienceLibrary sciencePopulationComputer science

Abstract

fetched live from OpenAlex

Objectives: The current study examines the experiences of gay, bisexual, two-spirit and other men who have sex with men (GB2M) who use networking applications and their engagements with online sexual health outreach workers disseminating healthcare information through these digital spaces. Methods: The iCruise study was a longitudinal mixed-methods study across Ontario, Canada which collected data on online sexual health information seeking behaviors. Results: Results offer insight into differences in information seeking behaviors among diverse groups of queer men. Conclusions: Implications for the dissemination of health information based on the results of information seeking patterns is discussed as well.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0040.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.107
GPT teacher head0.468
Teacher spread0.361 · 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

Citations20
Published2021
Admission routes3
Has abstractyes

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