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Record W2601286277 · doi:10.3138/jcfs.40.2.293

Culturally Diverse Elders and Their Families: Examining the Need for Culturally Competent Services

2009· article· en· W2601286277 on OpenAlexvenueno aff
Bahira Sherif Trask, Bethany Willis Hepp, Barbara H. Settles, Lilianah Shabo

Bibliographic record

VenueJournal of Comparative Family Studies · 2009
Typearticle
Languageen
FieldHealth Professions
TopicGeriatric Care and Nursing Homes
Canadian institutionsnot available
Fundersnot available
KeywordsEthnic groupDiversity (politics)ScholarshipCultural diversityPopulationService providerSociologyRace (biology)Service (business)Gender studiesEconomic growthBusinessAnthropology

Abstract

fetched live from OpenAlex

Demographic shifts among the aging population of the United States call for a re-examination of our understanding of the needs of these individuals, especially when race, ethnicity, family composition, and country of origin are considered in the discourse. This paper examines some of the implications of the rapid increase in racial and ethnic diversity among the older population in the United States for delivering culturally competent care through community based service providers. As much of our population ages, families across cultures and classes will increasingly need to be involved with specialized service providers. An ecological approach to this issue posits that elders, their families, and communities are closely intertwined, and need to be examined in relationship to one another. Issues such as race, ethnicity and culture of origin are part of this mix. Nevertheless, the family field, in particular, has been slow in examining the intersections between family, community supports and diversity. This paper highlights this phenomenon in order to spur an interest among scholars and practitioners in expanding this topic. Only with adequate scholarship and discussion will the appropriate delivery of much needed services to these culturally diverse elders and their families become a central component of the family field.

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 score0.133
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.163
GPT teacher head0.426
Teacher spread0.264 · 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

Citations8
Published2009
Admission routes1
Has abstractyes

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