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Record W4391362719 · doi:10.5206/ijoh.2023.3.15089

Challenges and Opportunities Experienced by Service Providers at Homeless Shelters in Tshwane, South Africa During the COVID-19 Pandemic

2024· article· en· W4391362719 on OpenAlexvenueno aff
Rivonia Mathole, Eleanor Ross

Bibliographic record

VenueInternational Journal on Homelessness · 2024
Typearticle
Languageen
FieldHealth Professions
TopicHomelessness and Social Issues
Canadian institutionsnot available
Fundersnot available
KeywordsCoronavirus disease 2019 (COVID-19)Pandemic2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Service providerService (business)BusinessInternet privacyEconomic growthMedicineVirologyMarketingComputer scienceOutbreak

Abstract

fetched live from OpenAlex

While the coronavirus disease of 2019 (COVID-19) pandemic exerted a devastating impact on all members of society, it highlighted the worsening inequalities experienced by marginalised groups such as people experiencing poverty and homelessness in South Africa. The pandemic also exacerbated the multiple demands and stressors of service providers working in homeless shelters. Hence, the study examined the experiences of service providers in terms of challenges and opportunities derived from working at homeless shelters in Tshwane, South Africa, during the hard lockdown from 27 March to 30 April 2020. Guided by a qualitative approach, five service providers were interviewed online. Thematic analysis revealed organizational challenges, such as a lack of knowledge and preparedness regarding disaster management, while client challenges included getting service users to adapt to living with rules. Organizational opportunities included the opportunity for the organization to conduct research, while personal opportunities included the learning experience of working with a vulnerable population during an unprecedented pandemic. Findings highlight the need for programmes to support the wellness needs of service providers and the development of a national policy on homelessness.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.893

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.169
GPT teacher head0.412
Teacher spread0.244 · 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 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".

Quick stats

Citations3
Published2024
Admission routes1
Has abstractyes

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