Children at Risk for Developing Reading Difficulties
Bibliographic record
Abstract
This article reports two different experiments as part of a longitudinal study. The first experiment examines the long-term efficacy of two brief remedial procedures (a Meaning-Based procedure versus a cognitive remediation program, the PASS Reading Enhancement Program [PREP]) focusing on the differences in phonological and cognitive test performance of 40 children who needed remediation for poor word decoding in Grade 1. The second study reports the outcomes of an intensified version of the PREP program that emphasizes strengthening the cognitive processes underlying reading in a remaining group of 24 difficult-to-remediate Grade 2 students. Follow-up data are also reported for this group. The results of the first study indicated that the PREP group improved significantly more in pseudo-word reading compared to the meaning-based group right after remediation in Grade 1. These differences, however, were somewhat reduced when re-testing occurred in Grade 2 and may be due to the influence of classroom instruction. Indeed, when both groups were compared to the norming sample, they appeared to continue to develop at an accelerated rate. As for the results of the second study, which focused on the longitudinal development of those 24 children who exhibited a history of chronic low reading performance, it was shown that PREP remediation kept producing significant gains, especially in word-decoding, a finding consistent with the theoretical framework of PREP.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".