Evidence map›Paper›PMID 41252404›Full record

ArticlePloS one2025

Learning deficits and early school leaving: Evidence from a longitudinal study in India.

K G Santhya, Nicole A Haberland, A J Francis Zavier

Abstract read
In one paragraph

Article in PloS one, 2025. 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
–field-weighted citation impact
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.

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.

K G SanthyaIndependent researcher, Kollam, India.ORCID https://orcid.org/0009-0003-5339-4314
Nicole A HaberlandDepartment of Population and Family Health, Columbia University Mailman School of Public Health, New York, New York, United States of America.
A J Francis ZavierPopulation Research Centre, Gandhigram, India.

Funding

Gates Foundation INV-021255
6 · The paper itself

Abstract

Although the relationship between learning deficits (LD) and early school leaving (ESL) is extensively acknowledged in studies from the Global North, fewer studies from the Global South have examined this relationship. We examined the levels and patterns of ESL among adolescents, relationship between LD and ESL and the gender dimensions, if any, in this relationship in India. We used data from a state-representative longitudinal study of adolescents aged 10-19 in Bihar and Uttar Pradesh states of India, conducted in 2015-16 and 2018-19. Descriptive analysis drew on data from adolescents ever enrolled in school (N = 11,476) and multivariate analyses used data from adolescents enrolled in school at wave 1 (N = 9,169). We used discrete-time hazard and fixed effects regression models to examine the relationship between LD and ESL. The probability of ESL was 39%, with a higher probability for girls (42%) than boys (38%). Although learning levels improved over time, 53% of adolescents displayed moderate or severe LD. Discrete-time hazard models show that LD influenced the probability of ESL (β = 1.959, p < 0.001 for those with severe LD and β = 0.568, p < 0.001 for those with moderate LD). Learning deficits equally affected the probability of ESL among girls and boys. Fixed effects regression models reiterate these findings. Investment in improving foundational skills is paramount for preventing early school leaving for girls and boys. However, the potential benefits will only be fully realised with accompanying measures which address gendered beliefs and practices and premature transition to adult roles, enhance parental engagement and improve education systems.

Indexed as

Learning DisabilitiesAdolescentChildFemaleHumansIndiaLongitudinal StudiesMaleSchoolsYoung Adult

Identifiers

PMID41252404
PMCPMC12626265

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.