Determinants of self-efficacy in patients with Parkinson’s disease
Bibliographic record
Abstract
BACKGROUND: Self-efficacy is the individual's assessment of his or hers ability to complete a specific task successfully and has been closely related to self-management and quality of life in several diseases. OBJECTIVE: To investigate self-efficacy in a population of Parkinson's disease (PD) patients in Mexico and study the factors that are associated with this measure. METHODS: We carried out a cross-sectional observational study involving patients with PD in an outpatient neurology clinic in Mexico, using the following instruments: Spanish version of the Chronic Disease Self-Efficacy Scale (CDSES), Quality of Life Questionnaire PDQ-8, Movement Disorders Society-Unified Parkinson's disease Rating Scale (MDS-UPDRS), Montreal Cognitive Assessment (MoCA), and Non-Motor Symptom Scale (NMSS). Clinical and demographic variables were also recorded. RESULTS: We included 73 patients with a mean age of 65 years and most patients were male. Patients with lower CDSES scores (<7.75) had worse scores in MDS-UPDRS, NMSS, and PDQ-8 scales. CDSES scores were significantly correlated with MDS-UPDRS Part I (r=-0.497, p=<0.001), Part II (r= -0.271, p=0.020), Part III (r=-0.304, p=<0.001), PDQ-8 (r=-0.472, p=<0.001), and NMSS (r=-0.504, p=<0.001). Furthermore, when assessing the simultaneous effect of covariates associated with CDSES score, only Mood/Apathy domain of NMSS was significant (beta= -0.446, t= -3.807, p= 0.012). CONCLUSIONS: PD patients with lower self-efficacy scores had worse motor and non-motor symptomatology and quality of life. Mood/Apathy disorders were negatively associated with self-efficacy and contributed significantly to this measure.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".