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Effective Use of Virtual Communities for Health-Purposes in the Elderly

2017· book-chapter· en· W2755963215 on OpenAlexaff
Elisabeth Beaunoyer, Matthieu J. Guitton

Bibliographic record

VenueAdvances in healthcare information systems and administration book series · 2017
Typebook-chapter
Languageen
FieldSocial Sciences
TopicTechnology Use by Older Adults
Canadian institutionsUniversité Laval
Fundersnot available
KeywordseHealthVirtual communityHealth careIsolation (microbiology)Social isolationInternet privacyKnowledge managementPublic relationsComputer scienceBusinessPsychologyWorld Wide WebThe InternetPolitical science

Abstract

fetched live from OpenAlex

New strategies must be developed to adequately answer the growing health needs of the elderly. Virtual communities targeted to older adults may represent interesting tools, ranging from providing health-related information to operating preventive programs, or simply reducing social isolation and thus increasing the quality of life of the elderly. Besides generic issues shared with any eHealth virtual community (e.g., user engagement, participation, acceptance), the use of virtual communities by the elderly also bears some specific challenges, including limitations related to access to care or the presence of individuals with (age-related) disability. This Chapter describes some of the factors which should be taken into consideration when designing eHealth strategies aiming at developing or supporting virtual communities targeted to elderly population, and emphases the importance of integrating health-oriented senior-targeted virtual communities into holistic approach to allow for the communities to optimally develop and consolidate, and reach its goal in terms of health benefits.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.966
Threshold uncertainty score0.908

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.006
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 head0.037
GPT teacher head0.345
Teacher spread0.308 · 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 designNot applicable
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

Citations4
Published2017
Admission routes1
Has abstractyes

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