Does Efficacy Equal Lasting Impact? A Study of Intervention Short Term Gains, Impact on Diagnostic Status, and Association with Background Variables
Bibliographic record
Abstract
OBJECTIVE: This article examines the efficacy of language intervention services for monolingual and immigrant children in a public clinic in Montreal, Canada. Intervention is provided in French for a preset number of sessions regardless of intervention needs. The study assessed immediate gains after intervention, their maintenance over 2 months, and their effect on diagnostic status at both time points. METHODS: Participants included 15 children (57.7 months SD 7.8) diagnosed with developmental language disorder: 3 monolinguals and 12 bilingual immigrants. Intervention targeted vocabulary and syntax. Assessment of intervention targets and standardized testing was conducted before, after, and 2 months after treatment. Diagnostic status and severity level were assessed at each time point. RESULTS: Intervention was highly efficacious with large effect sizes for intervention targets. However, for diagnostic status, efficacy was more questionable. Seven children improved their diagnostic status from pre- to posttest; but many dropped back at maintenance point. For 3 children, all test time showed scores within the non-impaired range, indicating misdiagnosis or change in status while waiting for treatment. Amount of pain was not correlated with any background variable. CONCLUSIONS: Results suggest that intervention could be more efficacious by giving more therapy sessions and conducting evaluation and treatment closer in time.
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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 | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".