Getting it “right”: how collaborative relationships between people with disabilities and professionals can lead to the acquisition of needed assistive technology
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine the impact of a consumer-led equipment and device program [Equipment and Assistive Technology Initiative (EATI) in British Columbia, Canada] from the perspective of program participants. The importance of collaborative assessments for obtaining the right assistive technology (AT) for meeting an individual's needs is discussed in light of the program's participant-centered "Participation Model", or philosophy by which the program is structured. METHOD: A cross-sectional survey with participants and semi-structured interviews were conducted with participants (≥ 18 years) who held a range of disabilities. The survey asked participants to rank their AT and to identify the method by which they obtained the technology [by self, prescribed by a health professional or collaborative (self and professional)]. Interviews addressed participants' opinions about obtaining and using AT. RESULTS: In total, 357 people responded to the survey (17% response rate) and 16 people participated in the interviews. The highest ranking AT was assigned to devices assessed via a collaborative method (self = 31%, practitioner = 26%, collaborative = 43%; χ(2) (16,180) = 39.604, p < 0.001). CONCLUSIONS: Shared decision-making between health professionals and people with disabilities within the assessment process for assistive technology leads to what participants perceive as the right AT. IMPLICATIONS FOR REHABILITATION: Collaborative decision-making can lead to the selection of assistive technology that is considered needed and right for the individual. Person-centered philosophy associated with assistive technology assessment is contributing to attaining "the right" AT.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.013 | 0.018 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".