Evidence map›Paper›PMID 38601547›Full record

ArticleHeliyon2024

Impact of COVID-19 on school attendance in South Africa. Analysis of sociodemographic characteristics of learners.

Godfred Anakpo, Sanelise Nkungwana, Syden Mishi

Open access · goldAbstract read
In one paragraph

Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
3.2field-weighted citation impact, top 9% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 5 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors at 1 institution in 1 country.

Godfred AnakpoDepartment of Economics, Nelson Mandela University, South Africa.
Sanelise NkungwanaDepartment of Economics, Nelson Mandela University, South Africa.
Syden MishiDepartment of Economics, Nelson Mandela University, South Africa.
Nelson Mandela University · ZA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The outbreak of COVID-19 had resulted in the complete closure of schools in nearly all countries across the globe. However, reopening after prolonged closure may adversely affect the school attendance of learners, with long-term implications for life outcomes. Given the limited studies on the subject, this study aims at examining the impact of COVID-19 on the school attendance of learners in South Africa and how this outcome is exacerbated by underlying socioeconomic factors using National Income Dynamics Study-Coronavirus Rapid Mobile Survey data (NIDS-CRAM). The findings reveal that COVID-19 has led to a sharp drop of 48.2% in the school attendance of learners following the reopening of schools after the state of national disaster-based lockdown. The study has demonstrated that the impact of COVID-19 on the school attendance of learners varies according to socioeconomic factors such as gender, race, geographical area, family size, and the economic status of the learners' parents and provincial location. Sociodemographic characteristics such as African race, coloured race, Asian race, female gender, rural location, large household size, and Eastern Cape are associated with lower school attendance among learners. Based on these findings, educational-based policy needs to include these sociodemographic factors to promote a high school attendance rate among learners. This may include a shift from a blanket approach to geographical, racial, family, and gender-based interventions.

Indexed as

COVID-19EnrollmentLearnersSchool attendanceSociodemographic characteristicsSouth Africa

Identifiers

PMID38601547
PMCPMC11004647
OpenAlexW4393374762

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.