Canadian francophone school‐based <scp>SLTs</scp> ' perspectives on their involvement in a Tier 1 interactive reading program: Collateral benefits to the shift in practice
Bibliographic record
Abstract
Abstract Increasing use of tiered service delivery models that include universal, whole‐class programming, is reshaping school‐based speech‐language therapists (SLTs)’ roles in supporting inclusive education. Interactive reading programs, designed to enhance language and literacy skills of diverse learners, offer SLTs an evidence‐based means to promote inclusive practices. However, SLTs' experiences in implementing such programs requires further investigation, particularly within Canadian francophone contexts. This qualitative study examined the experiences of seven SLTs across two Canadian francophone contexts—minority and majority—about their roles in implementing an interactive reading program. Through semi‐structured interviews and focus groups, a reflexive thematic analysis revealed two overarching themes. The first, Key factors contributing to success co‐exist amidst challenges , included four primary themes: SLT‐teacher partnership as fundamental to effective collaboration; a centralized mandate and coordinated implementation of the program as key; the value of a well‐structured program while maintaining a flexible approach; and the challenging journey of overcoming resistance to change. The second, Unexpected benefits for school‐based SLT practice , highlighted three primary themes: benefits for SLTs' own professional development; benefits for SLTs' roles within the educational context; and paving the way for future opportunities. Study findings offer insights into facilitating SLTs' vital role in promoting inclusive education.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".