The making of occupation-based models and diagrams: History and semiotic analysis
Bibliographic record
Abstract
BACKGROUND.: Models provide a structure for organizing knowledge and facilitating learning and are upheld by occupational therapy as epitomizing the cornerstones of its practice. PURPOSE.: This article briefly examines the scientific history of occupation-based model development in the 1950s before addressing the process of conceptual model making in occupational therapy. Using the theory of semiosis, it explains and takes a critical perspective on conceptual model building in occupational therapy. KEY ISSUES.: Since the surge of development in the mid-1970s, models have grown and undergone some revision. However, while the profession has often contested the definitions of its core terms, it has not challenged the accepted models and diagrams that present the constituents of practice. IMPLICATIONS.: Examining the processes of conceptual model development from a critical, semiotic point of view foregrounds models in the historico-theoretical literature and brings into scrutiny a model's relevancy in current practice.
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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 | 0.012 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.004 | 0.035 |
| Scholarly communication | 0.013 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".