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Health information triangulation: A complex and agentic practice among young parents

2015· article· en· W2501590073 on OpenAlexafffundabout
Devon Greyson

Bibliographic record

VenueProceedings of the Association for Information Science and Technology · 2015
Typearticle
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsUniversity of British Columbia
FundersCanadian Institutes of Health ResearchMedical Library Association
KeywordsTriangulationPsychologyPerspective (graphical)The InternetInformation seekingClosure (psychology)DeferenceSocial psychologyPublic relationsSociologyComputer sciencePolitical scienceGeographyWorld Wide WebLawArtificial intelligenceInformation retrieval

Abstract

fetched live from OpenAlex

ABSTRACT In situations of contested knowledge, information seekers may engage in triangulation practices in order to assess which information is right to meet their needs. Triangulation has been much discussed among researchers, but less so among lay populations. This poster presents results related to the triangulation practices of a group of young parents seeking health and parenting information in the Greater Vancouver region of Canada. Young mothers and fathers in this study described and demonstrated multiple types of triangulation in order to assess and make sense of both authoritative and non‐authoritative information. Seeking pathways were varied but could be classified as one of: a) escalating authoritativeness, b) second opinions, c) medical/non‐medical perspective, or d) inclusive triangulation. Engagement in triangulation practices was used both in deference to and as a mode of challenging medical authority. The dominant cultural emphasis on intensive parenting, coupled with widespread access to the Internet, made it possible and in some cases necessary for lay people to engage in “scientific” information practices such as triangulation.

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)0.010
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0100.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.017
Open science0.0000.000
Research integrity0.0000.000
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 head0.061
GPT teacher head0.415
Teacher spread0.355 · 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.

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

Citations7
Published2015
Admission routes3
Has abstractyes

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