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Record W2789315222 · doi:10.1177/1540415318757219

Latino Solo Grandparents Raising Grandchildren

2018· article· en· W2789315222 on OpenAlexaff
Deborah M. Whitley, Esme Fuller‐Thomson

Bibliographic record

VenueHispanic Health Care International · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGrandparentPsychological interventionMedicineAsthmaMental healthDepression (economics)GerontologyLogistic regressionPsychologyClinical psychologyPsychiatryDevelopmental psychology

Abstract

fetched live from OpenAlex

INTRODUCTION: The purpose of this descriptive report is to provide the first representative information on the sociodemographic profile and the prevalence of mental and physical health conditions of two "at-risk" groups of Latino caregivers: solo grandparent caregivers and single parents. METHODS: The 2012 Behavior Risk Factor Surveillance System was used to compare five dimensions of health on a sample of Latino solo grandparents and Latino single parents, raising their grandchildren/children alone. Bivariate and logistic regression comparative analyses were conducted on study measures. RESULTS: Latino solo grandparents have a high prevalence of chronic health conditions, including arthritis (51%), depression (40%), diabetes (34%), and asthma (34%). Latino single parents have lower but troubling health risks, including depression (22%), diabetes (14%), and asthma (14%). Differences between the two groups were largely due to the grandparents older age. DISCUSSION: Latino solo grandparents have a high prevalence of several chronic medical conditions. The prevalence of disorders is much lower for Latino single parents, although they too have disturbing health risks. CONCLUSION: Latino solo grandparents perform their parenting role under intense physical and emotional strain. Health professionals can be instrumental in facilitating interventions that affect the well-being of this expanding family group.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.840
Threshold uncertainty score0.894

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0010.000

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 head0.020
GPT teacher head0.358
Teacher spread0.339 · 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 designNot applicable
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

Citations14
Published2018
Admission routes1
Has abstractyes

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