Evidence map›Paper›PMID 32864426›Full record

ArticlePolicy insights from the behavioral and brain sciences2019

If we don't look, we won't see: Measuring language development to inform literacy instruction.

Suzanne M Adlof, Tiffany P Hogan

Abstract read
In one paragraph

Article in Policy insights from the behavioral and brain sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
24citing papers in PubMed, 1 pooled it
–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

24 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  9. Introduction to the Forum: Intersectional Approaches to Language in Autism.Perspectives of the ASHA special interest groups · 2025
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  19. Tense Marking in the Kindergarten Population: Testing the Bimodal Distribution Hypothesis.Journal of speech, language, and hearing research : JSLHR · 2021
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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

2 authors.

Suzanne M AdlofUniversity of South Carolina, Columbia SC.
Tiffany P HoganMGH Institute of Health Professions, Boston, MA.

Funding

Word Learning in Reading and Language Impairment SubgroupsR01DC017156 · NIDCD · UNIVERSITY OF SOUTH CAROLINA AT COLUMBIA · PI ADLOF, SUZANNE M. · 2018 to 2022
$3.7M
Orthography and phonology in word learning as a predictor of dyslexia in children with language impairmentR01DC016895 · NIDCD · MGH INSTITUTE OF HEALTH PROFESSIONS · PI HOGAN, TIFFANY P, WOLTER, JULIE A. · 2018 to 2022
$3.4M
NIDCD NIH HHS R01 DC016895NIDCD NIH HHS R01 DC017156
6 · The paper itself

Abstract

Oral language abilities enable children to learn to read, and they predict future academic achievement and life outcomes. However, children with language impairment frequently go unidentified because schools do not systematically measure oral language development. Given that identification paves the way for treatment, schools should increase attention to oral language development, particularly within response to intervention (RTI) frameworks, which aim to prevent learning disabilities by identifying and intervening at early stages. Formal schooling should address language comprehension (in addition to word reading) to ensure an adequate foundation for future reading comprehension. In support, we overview the developmental relations between oral language abilities and reading skills, review current school-based assessment frameworks, and discuss how these frameworks can include language assessments. Measuring language skills early and often benefits not only those who have language impairment but also all children, as it documents language variability to inform differentiated instruction.

Identifiers

PMID32864426
PMCPMC7451960

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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.