Neuro-occupation: A self-organizing approach to conflate the brain, context, and occupation
Bibliographic record
Abstract
BACKGROUND.: was coined to conflate three distinctly different concepts: the brain, context, and occupation. Discussing neuro-occupation has been more of an academic exercise rather than cogently researched for everyday practice, perhaps due to the seemingly incongruity among the concepts. PURPOSE.: This article traces the self-organization approach, an assumption of complex systems, to understand how the concepts can be conflated. METHOD.: Deductive category application, a qualitative descriptive method for tracing theoretical assumptions, was drawn from the lived experiences of 11 Iranian participants with cerebrovascular accidents. Matrix construction aided collection of data for analysis. FINDINGS.: The self-organization approach, underpinning neuro-occupation, was shown to be traceable, explaining how occupational participation may be influenced by the brain circular causality and perturbations provided by the context. IMPLICATIONS.: By understanding the dynamics of self-organization, occupational therapists can identify and create salient features that may motivate and enable clients to enhance occupational participation.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".