The Fusion of Public Health and Speech-Language Therapy in Schools: Has the Time Come?
Bibliographic record
Abstract
BACKGROUND: The burden of communication disorders in school age children is substantial. These disorders are among the most prevalent in schools. Communication disorders are also linked to numerous sequalae, such as poor socio-emotional health, poor academic and professional outcomes, high levels of unemployment, and increased healthcare utilization and costs. Nevertheless, children with these disorders are among the least likely to receive services from a rehabilitation professional. In fact, more than half of school-aged children with a communication disorder do not receive any speech-language therapy support. Clearly, new approaches to communication disorders in schools are needed. METHODS: We conducted a narrative review of scholarly and applied literature in three parts. The first justifies the fusion of public health and speech-language therapy in schools. The second explores public health frameworks for speech-language therapy and compares the same to tiered service delivery models recently implemented within schools. The third addresses the implications of this fusion for research in communication disorders and for clinical practice. RESULTS: Traditional models of speech-language therapy clinical practice in schools are no longer sustainable given the current challenges posed by childhood communication disorders. A communication disability public health approach is needed to overcome these challenges. Tiered service delivery models provide an excellent opportunity to pivot speech-language therapy practice and research in schools towards a public health approach. CONCLUSION: Through this narrative review, we argue that the future of speech-language therapy in schools must take advantage of advances in public health to respond to current child health needs in our communities. We strongly recommend the fusion of public health and school-based speech-language therapy to address the communication health needs of future generations.
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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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| 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".