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Record W4401388821 · doi:10.1177/21676968241273319

Resilience to Depression Among Emerging Adults in South Africa: Insights From Digital Diaries

2024· article· en· W4401388821 on OpenAlexaff
Diane Levine, Linda Theron, Sadiyya Haffejee, Michael Ungar

Bibliographic record

VenueEmerging Adulthood · 2024
Typearticle
Languageen
FieldPsychology
TopicResilience and Mental Health
Canadian institutionsDalhousie University
FundersBritish Academy
KeywordsPsychological resilienceCoping (psychology)Socioeconomic statusPsychologyMental healthCommunity resilienceGerontologyEnvironmental healthMedicineClinical psychologySocial psychologyResource (disambiguation)PsychiatryPopulation

Abstract

fetched live from OpenAlex

Emerging adults facing chronic socioeconomic stress, especially depression, lack comprehensive research on resilience factors. This study analyzed digital diary entries ( n = 338) from 57 individuals aged 18–24 in a South African township from July 2021 to April 2022. Participants highlighted relational, community, and cultural supports regardless of risk levels. Both high and low-risk groups faced challenges like financial instability, limited education, health threats, and lawlessness. However, institutional resource scarcity disproportionately affected higher-risk individuals, worsening issues like infrastructure deficits and violence exposure. Family and peer support emerged as crucial, especially for higher-risk participants. Individuals living in higher risk emphasized collective action and stranger support during infrastructure failures. These findings suggest that greater risk exposure may reinforce reliance on traditional, community-focused coping mechanisms, indicating the importance of studying differential resilience factors among young adults.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
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 head0.012
GPT teacher head0.310
Teacher spread0.298 · 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 source (direct Gemma or distilled Codex), 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

Citations2
Published2024
Admission routes1
Has abstractyes

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