Empowering Schools to Implement Effective Research-Based Reading Remediation Delivers Long-Lasting Improvements to Children’s Reading Trajectories
Bibliographic record
Abstract
There is a wide gap between what research evidence identifies as effective reading intervention and what is currently offered in schools. This effectiveness study reports the results of a long-term research/school system partnership that is implementing reading intervention for children with reading difficulties in Canadian community schools. In Study 1, growth-curve analyses revealed significant long-term shifts in the reading trajectories of children ( n = 731) from Kindergarten to Grade 5 as a function of receiving the Empower™ Reading: Decoding and Spelling intervention. Long-term outcomes were higher in children who received intervention in Grade 2 than in Grade 3, supporting the benefit of earlier intervention. In Study 2, we compare reading outcomes before and after children participated in school system-led intervention (Empower™ Reading, n = 341) to results from previously reported researcher-led intervention and business-as-usual controls. Children in both school system-led and researcher-led interventions showed greater improvement than controls on standardized measures of decoding and reading comprehension. Among school system participants, greater gains were seen for those with stronger reading skills at pre-test. Findings demonstrate successful school system implementation of research-originated and validated reading intervention. Researcher/school system partnerships may be integral in closing the research–practice gap.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".