When Functional Capacity and Real-World Functioning Converge: The Role of Self-Efficacy
Bibliographic record
Abstract
Although functional capacity is typically diminished, there is substantial heterogeneity in functional outcomes in schizophrenia. Motivational factors likely play a significant role in bridging the capacity-to-functioning gap. Self-efficacy theory suggests that although some individuals may have the capacity to perform functional behaviors, they may or may not have confidence they can successfully perform these behaviors in real-world settings. We hypothesized that the relationship between functional capacity and real-world functioning would be moderated by the individual's self-efficacy in a sample of 97 middle-aged and older adults with schizophrenia (mean age = 50.9 ± 6.5 years). Functional capacity was measured using the Brief UCSD Performance-based Skills Assessment (UPSA-B), self-efficacy with the Revised Self-Efficacy Scale, and Daily Functioning via the Specific Level of Functioning (SLOF) scale and self-report measures. Results indicated that when self-efficacy was low, the relationship between UPSA-B and SLOF scores was not significant (P = .727). However, when self efficacy was high, UPSA-B scores were significantly related to SLOF scores (P = .020). Similar results were observed for self-reported social and work functioning. These results suggest that motivational processes (ie, self-efficacy) may aid in understanding why some individuals have the capacity to function well but do not translate this capacity into real-world functioning. Furthermore, while improvement in capacity may be necessary for improved functioning in this population, it may not be sufficient when motivation is absent.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".