The Neuro-Occupation Model for Occupational Therapy: A Correlation Study
Bibliographic record
Abstract
Objectives: The Neuro-occupation model has been referenced as the single occupational therapy conceptual framework that considers the interaction of three underpinning variables of Intention, Meaning, and Perception that explains how occupational performance is shaped. To date, studies have focused on the qualitative relationships between the variables. Quantitative studies that focus on the relationships among variables are lacking, begging the question as to whether the model is well-conceptualized. Extending prior work on the Neuro-occupation model, the aim of this quantitative study is to test the model by investigating correlations among the key variables of the model. Methods: This is a correlational study by a convenience sample of 25 cognitively-oriented patients with strokes recruited from three rehabilitation facilities in Shiraz, Iran. The participants were evaluated using three standardized instruments to measure the variables: 1. Adapted Achievement Motivation Questionnaire, 2. Connor-Davidson Resilience Scale, and 3. Canadian Occupational Performance Measure. To control the effects of months post-stroke and cognitive functioning, the partial correlation test was used to explore relationships among the variables. Results: The correlational analysis indicated significant positive relationships among the variables of intention, meaning, and perception. The partial correlations showed acceptable correlation coefficients (r≥0.45, P<0.05). Discussion: The Neuro-occupation model is a well-conceptualized framework, which can assist occupational therapists in understanding the design of occupational performance.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".