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Record W3136550406 · doi:10.1177/08445621211004332

Finding the Silver Lining: Aging Well Amongst Older Brazilian Women in the Post-Migration Context

2021· article· en· W3136550406 on OpenAlexaffvenueabout
Stephanie Pedrotti Lucchese, Susan Bishop, Sepali Guruge, Margareth Santos Zanchetta, Diane Pirner

Bibliographic record

VenueCanadian Journal of Nursing Research · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Aging, and Tourism Studies
Canadian institutionsToronto Metropolitan UniversitySt. Michael's Hospital
Fundersnot available
KeywordsSnowball samplingContext (archaeology)Meaning (existential)ImmigrationFace (sociological concept)GerontologyGender studiesSociologyPopulation ageingPsychologyPopulationGeographyMedicineSocial scienceDemography

Abstract

fetched live from OpenAlex

STUDY BACKGROUND: The aging population in Canada has been increasing steadily over the past 40 years, however, there is limited information about the meaning of aging well amongst older Brazilian women in Canada. METHODS: A Heideggerian interpretive phenomenology study was conducted to understand the meaning of aging well amongst older Brazilian women in the post-migration context living in the Greater Toronto Area (GTA) in Ontario, Canada. RESULTS: Eight older Brazilian women residing in the GTA were recruited through purposive and snowball sampling and participated in individual face-to-face interviews. Through data analysis and the incorporation of Heidegger's four existentials of human existence, the themes that emerged were (a) Embracing being part of a mosaic, (b) Aging with grace, (c) Chasing your dreams and (d) Being a bridge and not a fence. The overarching theme was: Finding the silver lining: Aging well. CONCLUSION: This study informs nursing practice, research and policy development to advance the health of older immigrant adults in Canada.

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)0.007
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.065
GPT teacher head0.388
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.

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

Citations5
Published2021
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Nursing ResearchSame topicMigration, Aging, and Tourism StudiesFrench-language works237,207