Dynamic Performance Analysis: A Framework for Understanding Occupational Performance
Bibliographic record
Abstract
Occupational therapy is now consistently described as a profession concerned with enabling occupation. A crucial step in enabling occupation is understanding the occupational performance of our clients. Dynamic Performance Analysis (DPA) is a new approach to occupational analysis that focuses on the client's actual performance. DPA, acknowledging that optimal performance is the product of the interaction of person, environment, and occupation, and thus highly individualistic, places the client and his or her occupation, in interaction with the environment, at the center of the analysis process. Embedded in a top-down framework, DPA is a dynamic, iterative process, carried out as the client performs the occupation. The purpose of DPA is to identify where performance breaks down and test out solutions. In this article, the rationale, origins, and basic assumptions of DPA are discussed, and a detailed description of the DPA process together with two clinical examples is presented.
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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 | 0.011 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.016 | 0.015 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".