Predictors of unmet needs for communication and mobility assistive devices among youth with a disability: the role of socio-cultural factors
Bibliographic record
Abstract
PURPOSE: Despite the importance of ensuring access to assistive technology, high rates of underutilization remain. Relatively little is known about the characteristics of young people reporting unmet needs for assistive devices, so our study examined this further. METHOD: Data were analyzed using the 2006 Participation and Activity Limitation Survey. Youth aged 15-24 were selected to explore the characteristics associated with those currently using or reporting unmet needs for communication or mobility devices (n = 15,817). RESULTS: Family structure and language spoken influenced the likelihood of using a communication device for two age subgroups. Meanwhile, language spoken influenced the likelihood of reporting unmet needs for communication assistive devices. The following factors influenced the likelihood of using a mobility device: age, gender, language spoken, income, family structure, and severity of impairment. Gender, geographic location, language spoken, family structure, duration and severity of impairment and presence of other impairments influenced the likelihood of reporting unmet needs for mobility devices. CONCLUSIONS: Clinicians need to pay particular attention to the socio-cultural factors of young clients transitioning to adult care.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".