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Record W2032140116 · doi:10.12927/hcpap.2014.23868

Facilitating Consumer Access to Health Information

2014· letter· en· W2032140116 on OpenAlexaffvenueabout
Anne Snowdon, Karin Schnarr, Charles Alessi

Bibliographic record

VenueA Nudge Too Far? A Nudge at All? On Paying People to Be Healthy · 2014
Typeletter
Languageen
FieldHealth Professions
TopicHealth Literacy and Information Accessibility
Canadian institutionsWestern University
Fundersnot available
KeywordsHealth informationInternet privacyHealth information technologyBusinessInformation accessAccess to informationPersonally identifiable informationKnowledge managementComputer sciencePublic relationsMarketingData scienceHealth careWorld Wide WebComputer securityPolitical science

Abstract

fetched live from OpenAlex

The lead paper from Zelmer and Hagens details the substantive evolution occurring in health information technologies that has the potential to transform the relationship between consumers, health practitioners and health systems. In this commentary, the authors suggest that Canada is experiencing a shift in consumer behaviour toward a desire to actively manage one's health and wellness that is being facilitated through the advent of health applications on mobile and online technologies platforms. The result is that Canadians are now able to create personalized health solutions based on their individual health values and goals. However, before Canadians are able to derive a personal health benefit from these rapid changes in information technology, they require and are increasingly demanding greater real-time access to their own health information to better inform decision-making, as well as interoperability between their personal health tracking systems and those of their health practitioner team.

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)0.014
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.108
Threshold uncertainty score0.214

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.060
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0130.011
Scholarly communication0.0090.010
Open science0.0030.009
Research integrity0.0590.030
Insufficient payload (model declined to judge)0.0100.002

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 head0.124
GPT teacher head0.453
Teacher spread0.329 · 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 designNot applicable
Domainnot available
GenreCommentary

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

Citations2
Published2014
Admission routes3
Has abstractyes

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