Occupational Therapy Research on Assistive Technology and Physical Environmental Issues: A Literature Review
Bibliographic record
Abstract
BACKGROUND: To determine future directions for research in the area of assistive technology and physical environmental issues, it is important to have an understanding of prior research. PURPOSE: This literature review examined how assistive technology and physical environmental issues have been studied in the research published in international peer-reviewed occupational therapy journals. METHOD: Five recent volumes of nine journals were manually searched utilizing specific criteria. The publications were classified according to their perspective, application of the Person-Environment-Occupation (PEO) model, and the research design. RESULTS: Both research fields demonstrated use of different research methods and they displayed equal needs with regard to improved research methodologies. PRACTICE IMPLICATIONS: There are a lack of studies involving all three PEO components indicating a lack of research in occupational performance issues. Further research on occupational performance is important for developing occupational therapy practice in the area of assistive technology and physical environmental issues. Furthermore, study designs reflecting the societal level in all three PEO components are required. Finally, there is a strong need for conceptual and theoretical development in both fields.
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.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.013 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".