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Record W3173772358 · doi:10.1080/00909882.2021.1934513

Spanning communication boundaries to address health inequalities: the role of community connectors and social media

2021· article· en· W3173772358 on OpenAlexaff
Carolyn Wallace, Anthony McCosker, Jane Farmer, Carolynne White

Bibliographic record

VenueJournal of Applied Communication Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicSocial Media in Health Education
Canadian institutionsImpact
Fundersnot available
KeywordsPublic relationsHealth careSocial mediaFace (sociological concept)Qualitative researchHealth promotionSociologyBusinessPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Healthcare services face persistent barriers in reaching and improving access for marginalized or hardly reached people. This paper details the strategic use of social media among community connectors, or members of a community who address health issues in their local contexts by spanning socio-cultural boundaries within the community. Undertaking qualitative research in rural Australia and Ireland, we find evidence that through their strategic use of social media, connectors enhance the local communication infrastructure and create bridges between community and healthcare services due to their: (1) established connections with diverse hardly reached socio-cultural groups; (2) understanding of needs and opportunities for hardly reached people; (3) ability to seek out, ‘translate’ and pass on health information and (4) community building and health promotion practices at individual and social levels. Healthcare access and communication can be improved by identifying connectors and partnering with them to span boundaries enabling connection with the hardly reached.

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)0.021
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0210.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0060.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
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 head0.349
GPT teacher head0.517
Teacher spread0.168 · 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 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

Citations15
Published2021
Admission routes1
Has abstractyes

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