MétaCan
Menu
Back to cohort
Record W608507990

Why Should I Provide Social Support? A Social Capital Perspective of Individual Helping Behavior in Healthcare Virtual Support Communities

2014· article· en· W608507990 on OpenAlexaff
Kuang-Yuan Huang, Shobha Chengalur-Smith, Alain Pinsonneault

Bibliographic record

VenueJournal of the Association for Information Systems · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicKnowledge Management and Sharing
Canadian institutionsMcGill University
Fundersnot available
KeywordsSocial capitalSocial supportPerspective (graphical)Knowledge managementVirtual communityPsychologyPublic relationsSociologySocial psychologyBusinessComputer scienceWorld Wide WebPolitical scienceThe InternetSocial science
DOInot available

Abstract

fetched live from OpenAlex

The phenomenon of online social support has been studied for years. However, little is known about the factors that drive individual online helping behavior. While the Information systems literature provides rich insights into the determinants of online social support, the emphasis has been exclusively on the provision of informational help. By contending the need to expand our investigation to different types of support, this paper studies individual provisions of both informational and emotional social support in healthcare virtual support communities (HVSCs). Drawing on social capital theory, the structural, relational, and cognitive dimensions of social capital are conceptualized as the social support determinants. The results show that the social capital dimensions can be both facilitators and inhibitors of the two types of social support. This study can contribute not only to the literature on HVSCs, but also to studies of other types of virtual communities such as electronic networks of practice.

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)0.006
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.171
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
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 head0.054
GPT teacher head0.329
Teacher spread0.275 · 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.

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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the Association for Information SystemsSame topicKnowledge Management and SharingFrench-language works237,207