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Record W4256605079 · doi:10.32920/ryerson.14647434

Exploring the experience of loneliness among older Sinhalese immigrant women in Canada

2021· preprint· en· W4256605079 on OpenAlexaffabout
AV Pramuditha Madhavi

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsLonelinessPsychosocialPrivilege (computing)FeelingImmigrationGerontologyPsychologyOppressionOlder peopleSocial isolationMedicineSocial psychologyPsychiatryPolitical science

Abstract

fetched live from OpenAlex

Although people can become lonely at any age, older people are more likely to experience loneliness due to changes and losses that accompany aging. Older women are more likely to be lonely owing to their greater longevity as compared to older men (Hall & Havens, 1999). This situation can be worse for older immigrant women (Guruge, Kanthasamy & Santos, 2007). The purpose of this study was to uncover the experience of loneliness among older Sinhalese women in Toronto. Using a narrative inquiry approach (Clandinin & Clonnelly, 2000), I conducted in-depth individual interviews with two Sinhalese immigrant women. Study findings show that their feelings of loneliness were triggered due to loss of status and privilege, declined social network, negative role transition, and family oppression. Practice implications can include: work towards strengthening and expanding older immigrant women"s social network in order to create a conducive environment for psychosocial health and wellbeing.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.138
Threshold uncertainty score0.277

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0190.004
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.076
GPT teacher head0.317
Teacher spread0.241 · 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 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

Citations6
Published2021
Admission routes2
Has abstractyes

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