Direct assessment of emotional well-being from children with severe motor and communication impairment: a systematic review
Bibliographic record
Abstract
PURPOSE: Explore methods used in peer-reviewed literature for obtaining self-expression of well-being information from children with severe motor and communication impairment (SMCI). MATERIALS AND METHODS: A comprehensive search was conducted on 22 August 2019 through academic databases: CINAHL; Embase; MEDLINE; PsycINFO; InSpec; Compendex. Search strategies were informed by keywords under the following areas: (1) population: children with SMCI, (2) assessment methods: alternative to natural speech, paper and pencil report or standardized keyboard use (e.g., eye gaze) and (3) target information: well-being (e.g., quality of life). Studies were excluded if they focused on individuals over 25-years old, exclusively autism or typically developing children. RESULTS: = 17). Familiar partners play a significant role in self-expression; 18 studies required a familiar partner for children with SMCI to self-express. Thirty-five studies involved children self-expressing to solely adults, in comparison to 14 studies which involved peers. CONCLUSION: Findings highlight the advancement of high-tech communication devices restricted to application in single contexts. Familiar partner knowledge of children with SMCI has the potential to be shared with others (e.g., respite care providers), enhancing both caregiver and child well-being. Future research that would enhance the literature could explore the assessment of emotional well-being for application in various contexts using multimodal methods. Opportunities for children with SMCI to express their emotional well-being can further influence the understanding and enhancement of participation, social connections, and experiences.IMPLICATIONS FOR REHABILITATIONUse of lower tech methods of self-expression to obtain information directly from children with severe motor and communication impairment (SMCI) remain more feasible in home and school contexts.By utilizing familiar partners' experiences and knowledge of the child, respite care providers, novel support workers, and others involved in the lives of children with SMCI can become further informed.Current high-tech methods for obtaining the emotional expressions of children with SMCI may benefit from incorporating multimodal approaches including lower tech methods, to be feasibly applied in real world contexts where well-being takes place.Further research on this topic is imperative to enable children with SMCI to self-express their emotional well-being which can enhance participation, activities, social connections, and experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".