Assessment of the Effect of Meaningful Occupations on Motivation by Orbitofrontal Cortex Activation Using Near-Infrared Spectroscopy
Bibliographic record
Abstract
BACKGROUND: Meaningful occupations are those perceived as important by an individual. Research on meaningful occupations relies on subjective data and requires qualitative inquiries. Therefore, assessing the meaning of occupations using objective methods is challenging. As orbitofrontal cortex (OFC) activation is part of the reward system network involved in motivation, it could aid in assessing the meaning of occupations. OBJECTIVE: We aimed to investigate the effect of meaningful occupations on motivation by measuring OFC activation using near-infrared spectroscopy (NIRS). METHODS: Eight young and healthy participants were enrolled in this study. The occupation was set as "cooking," and its importance was confirmed using the Canadian Occupational Performance Measure (COPM). NIRS was performed using an OEG-16 (Spectareteh Inc.). The target task involved watching a cooking video, while the control task consisted of looking at a "+" sign on a blank sheet of paper. OFC activation was measured based on changes in oxygenated hemoglobin (oxy-Hb) concentration using a block design. Participants with COPM scores of eight or more were classified into the "meaningful occupation performance" group, while those with scores of seven or lower were classified into the "meaningful occupation non-performance" group. Changes in oxy-Hb concentrations between the two groups were compared using the Mann-Whitney U test. RESULTS: Four participants were assigned to the meaningful occupation group (frequency of implementation: various times per week for all participants), and four participants were assigned to the meaningful occupation non-performance group (frequency of implementation: various times per week for one participant, various times per month for one participant, and various times per year for two participants). Statistical analysis revealed significant differences in the changes in the oxy-Hb concentration in the left and bilateral OFC. CONCLUSION: This study suggests that it is important to focus on meaningful occupations that individuals consider important in order to activate the reward system and increase motivation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".