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Record W2137694206 · doi:10.1080/14927713.2009.9651450

Conceptualizing leisure self‐care in an exploratory study of American Indian Elders’ health beliefs and behaviours

2009· article· en· W2137694206 on OpenAlexaffvenue
Julie S. Son

Bibliographic record

VenueLeisure/Loisir · 2009
Typearticle
Languageen
FieldPsychology
TopicRecreation, Leisure, Wilderness Management
Canadian institutionsDalhousie University
Fundersnot available
KeywordsPsychosocialFocus groupGerontologyPsychologyExploratory researchHealth careSocial psychologyMedicineSociologyEconomic growthSocial science

Abstract

fetched live from OpenAlex

Abstract We describe the free‐time activities that emerged in the health beliefs and behaviours of American Indian elders in Northwestern Nevada utilizing a secondary data analysis of focus group data. Many American Indian health cultures maintain the need for a balance between body, mind, and spirit and represent a holistic view of health. We conducted focus groups with 19 American Indian elders aged 56 to 86 (17 women and two men) from three federally recognized tribes (two rural) and one urban tribal organization in Northern Nevada. The focus group data suggested that free‐time activities were an important aspect of the self‐care of elders living both on and off reservations in this geographical region. The elders’ health beliefs and behaviours centred on the physical and psychosocial benefits of being active/keeping busy during one's free‐time, social leisure, and leisure‐time physical activity, with multiple benefits described for some activities. The elders also described constraints to physical activities and arts and crafts primarily due to health limitations, as well as the negative aspects of free‐time particularly in regard to spending time with grandchildren and alcohol consumption. We propose that leisure self‐care may be a useful way to conceptualize the relationship between leisure and health as it relates to the opportunities, strategies, interests, and self‐identity of elders.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
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 head0.033
GPT teacher head0.345
Teacher spread0.313 · 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 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

Citations11
Published2009
Admission routes2
Has abstractyes

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