Assistive devices non-use, abandonment, or non-adherence? Toward standard terminology for assistive devices outcomes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it