MétaCan
Menu
Back to cohort
Record W2991453074 · doi:10.1177/0192513x19887524

Material and Relational Difficulties: The Impact of the Household Environment on the Emotional Well-Being of Young Black Women Living in Soweto, South Africa

2019· article· en· W2991453074 on OpenAlexafffund
Emmanuel Cohen, Lisa J. Ware, Alessandra Prioreschi, Catherine E. Draper, Edna Bosire, Stephen J. Lye, Shane A. Norris

Bibliographic record

VenueJournal of Family Issues · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Support in Illness
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsDisadvantagedSocioeconomic statusFocus groupBlack womenContext (archaeology)Psychological interventionPsychologyInequalitySocioeconomicsSociologyGeographyPopulationGender studiesEconomic growthDemography

Abstract

fetched live from OpenAlex

South Africa is an upper-middle income country with high levels of inequality. Young urban black women living in historically disadvantaged suburbs are particularly vulnerable to these socioeconomic inequalities. We conducted a qualitative study (four focus group discussions with young nulliparous women and 13 dual semistructured interviews between women and their mother) to better understand the household environment context of young women living in Soweto (a poor urban predominantly black township) and how this impacts their emotional well-being. Several household-centered issues were identified that impacted the young women’s well-being including both material and relational elements. These issues resulted in household environmental perturbations involving several psychological disturbances (stress, chronic anger, depression, and suicidal thoughts) stated by young women. Urban young black women experience significant material and relational hardships within the household environment. Interventions that aim to optimize young women’s emotional well-being should better recognize both economic and cultural aspects impacting on them.

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)0.002
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 score0.341
Threshold uncertainty score0.326

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.024
GPT teacher head0.253
Teacher spread0.229 · 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

Citations27
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Family IssuesSame topicFamily Support in IllnessFrench-language works237,207