Long-term effects of childhood speech and language disorders: A scoping review
Bibliographic record
Abstract
Background: Speech and language disorders in childhood have the potential to affect every aspect of a child’s day-to-day life and can potentially have negative long-term impacts. Aim: This scoping review seeks to collate the existing evidence to identify the long-term effects of childhood speech and language disorders. Methods: A systematic search of speechBITE, ERIC (Education Resources Information Center), Linguistics and Language Behaviour Abstracts, PubMed, MEDLINE, PsycINFO, SocINDEX and the Cochrane Library was conducted. Peer-reviewed English language publications reporting on the long-term (2+-year) outcomes of individuals with a childhood history of speech or language disorders were included. Data were extracted and the study quality assessed using a modified Newcastle–Ottawa scale. Results: Fifty-one studies met the inclusion criteria. These studies reported mixed results, the most common of which were suboptimal mental health, social and academic outcomes for persons with a history of speech or language disorders. We found an association between childhood speech or language disorders and psychiatric disability, behavioural problems, lower socio-economic status, relationship and living difficulties, and lower academic achievement compared to the general population. Conclusion: Individuals with a history of childhood speech or language disorders may experience long-term difficulties in mental health, social well-being and academic outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".