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Record W2900321894 · doi:10.1093/geroni/igy023.629

INTEREST GROUP SESSION - INDIGENOUS PEOPLES AND AGING: HEALTH BEHAVIORS AMONG INDIGENOUS OLDER ADULTS

2018· article· en· W2900321894 on OpenAlexaboutno aff
Jordan Lewis, T Goins

Bibliographic record

VenueInnovation in Aging · 2018
Typearticle
Languageen
FieldArts and Humanities
TopicArt Therapy and Mental Health
Canadian institutionsnot available
Fundersnot available
KeywordsIndigenousAutonomyPresentation (obstetrics)GerontologyHealth carePacific islandersPsychologyMedicineSociologyPolitical scienceEthnic groupAnthropology

Abstract

fetched live from OpenAlex

Indigenous older adults are living longer and remaining involved with families and communities where they derive meaning and a sense of purpose. Indigenous older adults engage in a variety of health behaviors grounded in traditional and western beliefs that enable them to engage in self- care and care for others. This symposium will highlight health behaviors and activities of American Indians and Alaska Natives that provide them with a sense of autonomy and control over their health as well as provide them opportunities to incorporate cultural beliefs and remain engaged, age well, and remain active in life. Our first presentation will present findings from a phenomenological study that examined successful aging among Elders in Northwest Alaska. Our second presentation will discuss Indigenous older adults’ Perspectives on aging well in an urban community in Canada. Our third presentation will share findings on the feasibility and preliminary outcomes from a piloted American Indian Elder Creative Cultural Arts Program. Our fourth presentation will address the needs for palliative care and support among Pacific Islands, and share resources of particular value to peoples underrepresented in palliative care research. Our final presentation will present on the prevalence and correlates of associated comorbidities among Native Hawaiian and Pacific Islander (NHPI) aging populations and share results from the NHPI National Health Interview Survey, 2014.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.549
Threshold uncertainty score0.986

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.035
GPT teacher head0.308
Teacher spread0.273 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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