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Record W2803371216

We have a lot of information to share with each other. understanding the value of peer-based health information exchange

2010· article· en· W2803371216 on OpenAlexaboutno aff
Tiffany C. Veinot

Bibliographic record

VenueInformation Research · 2010
Typearticle
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsnot available
Fundersnot available
KeywordsExperiential learningPsychologyFeelingSocial psychologyValue (mathematics)Interpersonal communicationInformation exchangePersonally identifiable informationSimilarity (geometry)Social exchange theoryComputer science
DOInot available

Abstract

fetched live from OpenAlex

Introduction. This study investigated whether rural people with HIV/AIDS exchanged information with their peers and why they valued this process. Method. In-depth interviews and personal social network solicitation were conducted with thirty-four rural-dwelling persons with AIDS in Canada. Analysis. Personal networks were analysed statistically. Interview transcripts were coded using the constant comparison method. Data were analysed with the aid of social comparison theory and the concept of 'experiential information'. Results. Most participants were connected to at least one peer and many had increased their contact with others with AIDS after their diagnosis. They valued peer-based information exchange for the experiential information content they shared, their practical and emotional uses of this information and the positive feelings generated by interacting with peers. Conclusions. Experiential similarity may predict interpersonal information seeking when people are under illness-related stress. Given its perceived value, peer-based health information exchange should be supported. Implications for information practice 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 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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.809

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.001
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 head0.454
GPT teacher head0.512
Teacher spread0.058 · 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

Citations45
Published2010
Admission routes1
Has abstractyes

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