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Record W2732381930 · doi:10.5770/cgj.20.271

Caring in the Information Age: Personal Online Networks to Improve Caregiver Support

2017· article· en· W2732381930 on OpenAlexafffundvenue
Emily Piraino, Kerry Byrne, George Heckman, Paul Stolee

Bibliographic record

VenueCanadian Geriatrics Journal · 2017
Typearticle
Languageen
FieldHealth Professions
TopicFamily and Patient Care in Intensive Care Units
Canadian institutionsResearch Institute for AgingMcMaster UniversityUniversity of Waterloo
FundersCanadian Institutes of Health Research
KeywordsMedicineGerontologyInternet privacy

Abstract

fetched live from OpenAlex

BACKGROUND: It is becoming increasingly important to find ways for caregivers and service providers to collaborate. This study explored the potential for improving care and social support through shared online network use by family caregivers and service providers in home care. METHODS: [NY: Free Press; 1995], and involved focus group and individual interviews of service providers (n = 31) and family caregivers (n = 4). Interview transcriptions were analyzed using descriptive, topic, and analytic coding, followed by thematic analysis. RESULTS: The network was identified as presenting an opportunity to fill communication gaps presented by other modes of communication and further enhance engagement with families. Barriers included time limitations and policy-related restrictions, privacy, security, and information ownership. CONCLUSION: Online networks may help address longstanding home-care issues around communication and information-sharing. The success of online networks in home care requires support from care partners. Future research should pilot the use of online networks in home care using barrier and facilitator considerations from this study.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.001
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 head0.058
GPT teacher head0.344
Teacher spread0.286 · 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 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

Citations15
Published2017
Admission routes3
Has abstractyes

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