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Record W2764035245 · doi:10.1177/1049732317732962

“Your Brain Matters”: Issues of Risk and Responsibility in Online Dementia Prevention Information

2017· article· en· W2764035245 on OpenAlexfundno aff
Michael Lawless, Martha Augoustinos, Amanda LeCouteur

Bibliographic record

VenueQualitative Health Research · 2017
Typearticle
Languageen
FieldArts and Humanities
TopicLanguage, Discourse, Communication Strategies
Canadian institutionsnot available
FundersAlzheimer Society
KeywordsDementiaNormativeIdentity (music)Moral responsibilityPsychologyPublic relationsContext (archaeology)Health promotionPublic healthThe InternetPromotion (chess)Social psychologyMedicinePolitical scienceNursingLaw

Abstract

fetched live from OpenAlex

The Internet has been argued to provide diverse sites for health communication and promotion, including issues that constitute major public health priorities such as the prevention of dementia. In this study, discursive psychology is used to examine how information about dementia risk prevention was presented on the websites of the most prominent English-language, nonprofit dementia organizations. We demonstrate how information about dementia risk and its prevention positions audiences as at-risk of developing dementia and constructs preventive behavior as a matter of individual responsibility. Websites represented participation in certain lifestyle practices as normative and emphasized audience members' personal responsibility for managing dementia risk. It is argued that such representations promote a moral identity in regard to brain health in which an ethic of self-responsibility is central. The implications of such identity construction in a context of increasing prevalence of dementia diagnosis are discussed.

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)0.016
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.030
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0120.032
Scholarly communication0.0110.011
Open science0.0010.009
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0030.000

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 head0.486
GPT teacher head0.613
Teacher spread0.127 · 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 designQualitative
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

Citations41
Published2017
Admission routes1
Has abstractyes

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