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Record W4403659274 · doi:10.3389/fpubh.2024.1389099

Canadian perspectives on loneliness; digital communication as meaningful connection

2024· review· en· W4403659274 on OpenAlexaffabout
Lauren Dwyer

Bibliographic record

VenueFrontiers in Public Health · 2024
Typereview
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsMount Royal University
Fundersnot available
KeywordsLonelinessPerspective (graphical)Situational ethicsContext (archaeology)PsychologyCollectivismSocial isolationExperiential learningSocial psychologyVulnerability (computing)IndividualismComputer sciencePolitical sciencePsychotherapistArtificial intelligenceComputer security

Abstract

fetched live from OpenAlex

This perspective piece considers loneliness and its relationship to communication, connection, and technology by reviewing the origins and lessons from the field. It begins with a search for an operational definition, then examines the differences between experiential (situational/isolation-based) and existential (continuous, non-situational) loneliness. Technology is addressed as both a hindrance and a tool for alleviating loneliness with the example of companion robots as an emerging technology for loneliness mitigation. Cultural differences in experiences of loneliness, specifically as a public health issue, are in the context of the COVID-19 pandemic in Canada. Concepts of social and emotional loneliness, individualism and collectivism, socioeconomic status, vulnerability, and lived experience are explored and provide an emphasis on 'meaningful connection' in the study of loneliness.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.735
Threshold uncertainty score0.527

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.008
Science and technology studies0.0030.005
Scholarly communication0.0040.003
Open science0.0010.003
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.001

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 head0.084
GPT teacher head0.409
Teacher spread0.325 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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
Published2024
Admission routes2
Has abstractyes

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