Integrating the International Classification of Functioning, Disability and Health Contextual Factors and a Trauma Lens to Inform Speech-Language Pathologists' Practice With Children and Families
Bibliographic record
Abstract
PURPOSE: This article highlights how the World Health Organization's International Classification of Functioning, Disability and Health framework aligns with a trauma lens in speech-language pathology practice by considering risk and protective factors in a client's life and within clinical care interactions at individual, family, and community and population levels. This approach shifts practitioners from a traditional biomedical model to a holistic biopsychosocial model that considers the needs, strengths, and priorities of family members, in alignment with shifts in the field. Appropriate models of service delivery, the application of trauma-informed principles, and the need for changing systems and policies to promote equity in services are also discussed. CONCLUSIONS: By addressing both personal and environmental contextual factors, speech-language pathologists can better understand and support their clients' unique experiences and needs. This comprehensive understanding fosters a more inclusive, effective, and compassionate practice, ultimately enhancing the overall well-being and outcomes of clients.
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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 | 0.021 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.005 |
| 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".