Characteristics of assistive technology service delivery models: stakeholder perspectives and preferences
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to identify the key characteristics of an assistive technology service delivery model preferred by the various stakeholders in Manitoba, Canada. METHODS: A descriptive, exploratory approach consistent with qualitative research design was used to explore this issue. Three focus groups were held using a semi-structured interview guide and a hypothetical case study to guide the discussion. Eighteen adults participated in the study, each representing one of three groups of stakeholders (assistive technology service providers, funders and users). Interviews were audiotaped, transcribed and analysed using an inductive process to develop categories and themes. RESULTS: Three primary themes emerged from the data: the user of assistive technology is a unique individual; a decision-making process exists; and, assistive technology devices and services are complex. Based on the study results, recommendations for the delivery of assistive technology services are outlined. CONCLUSIONS: The results of this study may be useful for developing funding guidelines, supporting the importance of assistive technology in enabling meaningful activities, and examining current delivery of services in different contexts.
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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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".