The use of everyday technology in occupational therapy practice for clients with acquired brain injury
Bibliographic record
Abstract
The purpose of this descriptive study was to investigate how occupational therapists use everyday technology (ET) in their evaluation and treatment of adults with acquired brain injury (ABI). Questions included (1) the type of client therapists believed most likely to benefit from using technology, (2) current patterns of technology use with clients, including type of technology and frequency of use (3) the extent to which therapists think ET was effective, and (4) the supports for and barriers against using ET in practice. A survey was completed by 40 occupational therapists who were members of the Physical Disabilities, Technology, or Home and Community Health Special Interest Sections (SIS) of the American Occupational Therapy Association (AOTA). The findings indicated that occupational therapists tend not to ask questions about ET, evaluate its use formally or informally, may make assumptions about client’s ability to use ET, and not consider work related interventions. Many clinicians report ET to be useful, but tend not to use it in practice, possibly due to barriers impacting therapists’ use of ET, such as, access to and knowledge about ET. ET use should be considered in the future development of standardized assessments, occupational therapy education, and research.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".