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Aging, diversity, and health: the Brazilian and the Canadian context

2011· article· en· W2063446693 on OpenAlexaffabout
Lisiane Manganelli Girardi Paskulin, Marinês Aires, Ana Valéria Furquim Gonçalves, Carla Cristiane Becker Kottwitz, Eliane Pinheiro de Morais, Mario Brondani

Bibliographic record

VenueActa Paulista de Enfermagem · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicHealth disparities and outcomes
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsDiversity (politics)Ethnic groupContext (archaeology)ImmigrationPopulation ageingPsychological interventionHealth carePublic healthPhenomenonGerontologyEconomic growthCultural diversityPopulationPolitical scienceDevelopment economicsDemographic economicsGeographySociologyPsychologyMedicineDemographyEconomicsNursing

Abstract

fetched live from OpenAlex

Aging is a universal and yet diverse phenomenon. This paper presents a review on the topic of diversity in the context of the aging populations in Brazil and Canada. The diversity of the aging population in both countries is discussed in terms of gender, ethnicity, age groups and living conditions while considering the impact on the health care systems. Understanding and reflecting on the Brazilian and Canadian realities reinforces the need for respecting these diversities when developing and implementing local health policies and interventions. There are some similarities regarding gender, but marked differences in immigration patterns, education and living arrangements. The heterogeneity in the aging process within each country and between them carries different expectations and generates social consequences that manifest themselves in differences in health situations, resulting in new challenges to health services and the formulation of public policies for this age group in both countries.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.030
Threshold uncertainty score0.221

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.007
Science and technology studies0.0090.005
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.103
GPT teacher head0.339
Teacher spread0.236 · 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 designObservational
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

Citations13
Published2011
Admission routes2
Has abstractyes

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