Anatomy of Cognitive Strategies: A Therapist's Primer for Enabling Occupational Performance
Bibliographic record
Abstract
BACKGROUND: Promoting effective strategy use is an integral part of enabling occupational performance; however, there are variations in how strategies are defined, discussed, used, and applied in occupational therapy practice. PURPOSE: Focusing on cognitive strategies, in this paper, we define and describe strategies and their types and divide the concept of strategies into two dimensions: strategy attributes and strategy use. A comprehensive framework for each dimension (attribute and use) is proposed as a clinical reasoning guide as well as a foundation for future research. The frameworks are designed to reduce ambiguity, deepen understanding, and serve as clinical reasoning guides assisting therapists in specifying, describing, and observing cognitive strategies during occupational performance. KEY ISSUES: We argue that there is a need for therapists to use consistent terminology and to be able to systematically select cognitive strategies and evaluate their use. IMPLICATIONS: The proposed strategy frameworks provide clinical guides for systematic analysis and selection of cognitive strategies as well as for observing components of strategy use during clients' occupational performance. We suggest the need for greater specification and description of strategies during intervention and highlight directions for future 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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.005 | 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".