Assistive devices non-use, abandonment, or non-adherence? Toward standard terminology for assistive devices outcomes
Bibliographic record
Abstract
For individuals with disabilities, failure to use prescribed assistive technology devices (ATDs) according to professional recommendations can have detrimental health consequences. The literature has employed various terms to describe this phenomenon such as nonuse, abandonment, and non-adherence to characterize this behavior, lacking clear and standardized definitions. Consistent use of a standardized language is critical for advancing research in this area. This study aims to identify and describe the concepts related to the failure to use prescribed ATDs, along with the associated contexts, and proposes a framework for standardizing terminology in this domain. A narrative literature review encompassing studies from inception to June 2023 was conducted to elucidate these concepts. Out of 1029 initially identified articles, 27 were retained for in-depth analysis. The review unveiled a significant inconsistency in the use of terms like nonuse, abandonment, noncompliance, and non-adherence. Some articles even employed these terms interchangeably without clear definitions. Only 10 of the 27 reviewed articles provided definitions for the terminology they used. This highlights the crucial need for adopting valid conceptual models to select appropriate terms. Researchers are strongly encouraged to furnish operational definitions aligned with theoretical models and relevant to their research context to advance this field consistently.
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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 | 0.062 | 0.127 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.002 | 0.012 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 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".