Can a short parent questionnaire be helpful for correctly identifying children with and without specific language impairment?
Bibliographic record
Abstract
Language development is influenced by various factors related to intrinsic characteristics of the child, such as specific language impairment (SLI), or to his or her environment, such as a bilingual upbringing. However, it is in the presence of a language impairment that a child should receive language intervention. The purpose of the current thesis is to investigate the usefulness of the Milestones en français du Québec (MilBec: Paul & Elin Thordardottir, 2010), a short parent questionnaire for children between 12 and 71 months adapted from Luinge et al. (2006) to screen for SLI. In Chapter 1, the adaptation procedures are detailed and a cross-sectional study that led to the creation of the MilBec is presented, followed by a longitudinal study. The results showed that the MilBec is developmentally sensitive with a rapid increase up to 36-42 months, followed by a slowed increase ending with a ceiling effect for children 60 months and older. The MilBec's validity is supported by correlation analyses with other language tests, and high levels of reliability are suggested for test-retest and inter-judge reliability measures. In Chapter 2, three studies are presented: a preliminary normative study, a diagnostic accuracy study, and a study presenting the analysis of the children's performance on two items viewed as potential clinical markers, namely object clitic and subject use. Object clitics are pronouns used between the subject and the verb functioning as the direct object of the verb (e.g. "Je LE prends" ('I IT take')). The results indicated that the MilBec is developmentally sensitive and has a good diagnostic accuracy, with sensitivity of 100% and specificity of 93% at identifying children with a moderate to severe SLI. The concurrent validity of the MilBec is further supported by high correlations with various language tests. Finally, the analyses showed that when a parent reports that his or her child does not use the subject after the age of 27 months, or the object clitic after the age of 31 months, it is a strong sign of the presence of SLI (a specificity of 100% and 97%, respectively). However, if the child uses these elements, it cannot be used to rule out SLI, given the sensitivity of only 20% for subject use and 53% for object clitic use. In Chapter 3, the usefulness of the separation of the MilBec's the items into two subscales, depending on whether the items assess a language domain particularly affected, or unaffected, by bilingualism is explored. A pilot study comparing the performance of two groups of TD bilingual children, with either a high or a low exposure to French, showed that bilingual children generally perform similarly to monolingual children on the MilBec. However, a higher proportion of bilingual children with low exposure to French failed the subscale targeting domains more affected by bilingualism. In contrast, monolingual children with SLI failed both subscales.Together, the findings indicated that MilBec is a promising screening test to identify French-speaking children with a moderate to severe SLI, even for bilingual children. Future studies should be performed to collect norms from a population sample, to further investigate the performance of bilingual children, and to determine its predictive validity. Keywords: French, specific language impairment (SLI), test validation, normative data, parent-questionnaire, screener, bilingual, language acquisition
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".