A framework for the conceptual modelling of assistive technology device outcomes
Bibliographic record
Abstract
PURPOSE: A key step in planning assistive technology outcomes research is formulation of a conceptual model, specific to a particular type of device, that provides a rationale for the expected outcomes. This paper reflects the conviction that the development of device-specific causal models will be facilitated by having available an overarching framework that is potentially applicable to multifarious types of devices and their outcomes. METHOD: A literature review identified the critical, unmet needs for a conceptual framework. The assumptions underlying the framework were specified preparatory to describing it and discussing its implications. RESULTS: The outcomes of assistive technology devices are depicted as resulting from the interaction among characteristics of a specific device-type, its users, and their environment. Initial junctures include procurement of a type of device and a period of introductory use that, interacting with various moderating co-factors, result in a variety of shorter-term outcomes, possible longer-term use, and its outcomes. CONCLUSIONS: The framework has the potential of facilitating the development of device-specific causal models. It also may contribute to developing a research agenda for assistive technology outcomes research by highlighting measures that need to be developed and by identifying testable hypotheses concerned, for example, with the manner and duration of devices' usage.
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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 | 0.028 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".