âLit For Lifeâ: Using Literacy Intervention to Foster Meaningful Life Changes for High-risk Youth with Reading Disabilities
Bibliographic record
Abstract
Substantial evidence indicates that maltreatment places abused children at great risk for illiteracy and damaging self-perceptions of competency and worth. Given that academic ability and self-concept are reciprocally related and mutually reinforcing, it was hypothesized that participation in an intensive literacy intervention would positively impact the reading, writing, and related self-perceptions of maltreated Struggling Readers from the Ontario Child-Welfare system. Using a mixed methods approach, 24 participants (ages 14-24) completed achievement and self-perception measures and were interviewed about their literacy experiences and views, pre and post intervention. Repeated measures analyses and pairwise comparisons measured the impact of intervention on the literacy skills and related self-evaluations of these youth and assessed how the literacy skills and related self-evaluations differed from maltreated youth without reading difficulties (n = 22). Interviews were analyzed thematically. Results converged to provide empirical support for the benefits of literacy intervention on skill and self-perception development for this high risk group of youth. Qualitative analyses further revealed unanticipated, dramatic and meaningful life changes. Participants manifested improved communication and metacognitive skills, increased autonomy and internal motivation, and amplified feelings of empowerment and hope for the future.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".