Application of the Human Activity Assistive Technology model for occupational therapy research
Bibliographic record
Abstract
BACKGROUND/AIM: Theoretical models provide a framework for describing practice and integrating evidence into systematic research. There are few models that relate specifically to the provision of assistive technology in occupational therapy practice. The Human Activity Assistive Technology model is an enduring example that has continued to develop by integrating a social model of disability, concepts from occupational therapy theory and principles of assistive technology adoption and abandonment. METHODS: This study first describes the core concepts of the Human Activity Assistive Technology model and reviews its development over three successive published versions. A review of the research literature reflects application of the model to clinical practice, study design, outcome measure selection and interpretation of results, particularly among occupational therapists. An evaluative framework is used to critique the adequacy of the Human Activity Assistive Technology model for practice and research, exploring attributes of clarity, simplicity, generality, accessibility and importance. Finally, recommendations are proposed for continued development of the model and research applications. RESULTS: Most of the existing research literature employs the Human Activity Assistive Technology model for background and study design; there is emerging evidence to support the core concepts as predictive factors. Although the concepts are generally simple, clear and applicable to occupational therapy practice and research, evolving terminology and outcomes become more complex with the conflation of integrated theories. CONCLUSIONS AND SIGNIFICANCE: The development of the Human Activity Assistive Technology model offers enhanced access and application for occupational therapists, but poses challenges to clarity among concepts. Suggestions are made for further development and applications of the model.
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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 | 0.058 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".