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Record W3146945528 · doi:10.1017/s0144686x21000349

Experiences of loneliness among older people living alone. A qualitative study in Quebec (Canada)

2021· article· en· W3146945528 on OpenAlexaffabout
Michèle Charpentier, Laurie Kirouac

Bibliographic record

VenueAgeing and Society · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversité LavalUniversité du Québec à Montréal
Fundersnot available
KeywordsLonelinessSolitudeMarital statusPsychologyQualitative researchSocial isolationCompromiseGerontologySocial psychologyMedicineSociologyDemographyPopulationPsychiatry

Abstract

fetched live from OpenAlex

Abstract In this article, we analyse experiences of loneliness among older people living alone. Current knowledge suggests that loneliness is a significant social issue that can compromise health and wellbeing, and that seniors living alone are at a higher risk of loneliness. Based on a qualitative methodological approach and semi-structured interviews conducted with 43 people aged 65 or over living alone in Montreal (Quebec, Canada), this study sought to understand how they perceive, reflect on and react to loneliness. The results show that these seniors perceive loneliness as a dynamic, and rarely static, experience, which has a very different significance, depending on whether it is chosen or circumstantially imposed. The experience of loneliness recounted by the seniors we met is characterised by its heterogeneity, and involves, to varying degrees, their relationship to themselves ( solitude ), to others ( family (and friends) loneliness and loneliness in love ) and/or to the world ( existential loneliness and aloneness ). Lastly, our analyses highlight how social factors, such as age, gender, marital status, social network and socio-economic conditions, shape the experience of loneliness among seniors. These factors also influence the strategies that seniors develop to prevent or alleviate loneliness, strategies that yield very mixed results.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.222
Threshold uncertainty score0.291

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 studies0.0000.000
Scholarly communication0.0000.000
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 head0.023
GPT teacher head0.347
Teacher spread0.323 · 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

Citations22
Published2021
Admission routes2
Has abstractyes

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