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‘Life became harder with COVID-19’: exploring the experiences of the COVID-19 pandemic among youth living in eThekwini district, South Africa

2024· other· en· W6958737779 on OpenAlexaff

Bibliographic record

VenueFigshare · 2024
Typeother
Languageen
FieldChemistry
TopicAdvanced NMR Techniques and Applications
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsPandemicGovernment (linguistics)Thematic analysisPsychological interventionMental healthPublic healthCoronavirus disease 2019 (COVID-19)Public policy

Abstract

fetched live from OpenAlex

Abstract Background In South Africa, pervasive age and gender inequities have been exacerbated by the COVID-19 pandemic and public health response. We aimed to explore experiences of the COVID-19 pandemic among youth in eThekwini district, South Africa. Methods Between December 2021-May 2022 we explored experiences of the COVID-19 pandemic on youth aged 16–24 residing in eThekwini, South Africa. We collated responses to the open-ended question “Has the COVID-19 pandemic affected you in any other way you want to tell us about?” in an online survey focused on understanding the pandemic’s multi-levelled health and social effects. We used a thematic analysis to summarise the responses. Results Of 2,068 respondents, 256 (12.4%, median age = 22, 60.9% women) completed the open-ended survey question (11% in isiZulu). Results were organized into three main themes encompassing (1) COVID-19-related loss, fear, grief, and exacerbated mental and physical health concerns; (2) COVID-19-related intensified hardships, which contributed to financial, employment, food, education, and relationship insecurities for individuals and households; and (3) positive effects of the pandemic response, including the benefits of government policies and silver linings to government restrictions. Conclusions We found that South African youth experienced significant grief and multiple losses (e.g., death, income, job, and educational) during the COVID-19 pandemic. Trauma-aware interventions that provide economic and educational opportunities must be included in post-COVID recovery efforts.

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)0.006
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0140.009
Scholarly communication0.0060.006
Open science0.0010.008
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.000

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 head0.136
GPT teacher head0.320
Teacher spread0.183 · 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 designQualitative
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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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