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Record W1502958458 · doi:10.1300/j499v08n01_05

Orphans in Nyanza, Kenya

2007· article· en· W1502958458 on OpenAlexaff
Tamara Landry, Isaac Luginaah, Eleanor Maticka‐Tyndale, David Elkins

Bibliographic record

VenueJournal of HIV/AIDS Prevention in Children & Youth · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicPoverty, Education, and Child Welfare
Canadian institutionsUniversity of WindsorWestern University
Fundersnot available
KeywordsStressorCoping (psychology)PsychologyThematic analysisFocus groupHuman immunodeficiency virus (HIV)Developmental psychologyClinical psychologyQualitative researchSociologyMedicineFamily medicineSocial science

Abstract

fetched live from OpenAlex

Abstract This paper examined the everyday challenges, Stressors and coping strategies of orphans affected by HIV/AIDS in Nyanza, Kenya. A thematic analysis of six focus group discussions with orphans was guided by Stress and Coping Theoretical Framework. The orphans reported intense stress at the time of their parents' death with their immediate concern being who would care for them. Most orphans were separated from their siblings, and this separation only compounded the Stressors and difficulties encountered by orphans. Orphans reported having problems with schooling and being treated differently as compared with the children of their caregivers. Orphans adopted various emotion-foused and problem-focused coping strategies, which were reinforced by financial and social support provided by their caregivers and community-based organizations.

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)0.004
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.515

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.296
Teacher spread0.282 · 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 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
Published2007
Admission routes1
Has abstractyes

Explore more

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