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Record W2341482796 · doi:10.25300/misq/2016/40.1.11

The Creation of Social Value: Can An Online Health Community Reduce Rural–Urban Health Disparities?1

2016· article· en· W2341482796 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMIS Quarterly · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsValue (mathematics)Health equityPublic relationsBusinessEconomic growthPolitical scienceHealth careEconomicsComputer science

Abstract

fetched live from OpenAlex

The striking growth of online communities in recent years has sparked significant interest in understanding and quantifying benefits of participation. While research has begun to document the economic outcomes associated with online communities, quantifying the social value created in these collectives has been largely overlooked. This study proposes that online health communities create social value by addressing rural–urban health disparities via improved health capabilities. Using a unique data set from a rare disease community, we provide one of the first empirical studies of social value creation. Our quantitative analysis using exponential random graph models reveals patterns of social support exchanged between users and the variations in these patterns based on users’ location. We find that, overall, urban users are net suppliers of social support while rural participants are net recipients, suggesting that technology-mediated online health communities are able to alleviate rural–urban health disparities. This study advances extant understanding of value production in online collectives, and yields implications for policy.

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.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.530
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0040.001
Scholarly communication0.0000.000
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 head0.114
GPT teacher head0.428
Teacher spread0.314 · 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