Expertise in research-informed clinical decision making: Working effectively with families of children with little or no functional speech
Bibliographic record
Abstract
In this article, we consider what is known about how to work effectively with families of children with disabilities, including those with little or no functional speech. Existing evidence about what families want from services is considered, along with information about how expert therapists practice. Our review indicates the importance of understanding family needs, preferences, and priorities, and of being sensitive to the demands of interventions on family life. The augmentative and alternative communication (AAC) literature is linked to the broader literature, confirming what is known about how to work effectively with families and illuminating the contribution of AAC research to this area of knowledge. In general, the AAC literature highlights the importance of the parent-practitioner relationship, of parental involvement and engagement in the intervention process, and of considering the demands that interventions place on families. We conclude that AAC intervention will benefit from continuing therapist efforts to strengthen the client–practitioner relationship through greater situational understanding and appreciation of family perspectives and life circumstances. Therapists’ efforts should also focus on customizing intervention strategies in order to optimize clients’ sense of control, meaningfulness, and engagement.
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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 | 0.059 | 0.134 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".