Health Locus of Control and Its Relationship with Quality of Life and Functioning in Multiple Sclerosis: Exploring the Mediating Role of Self-Efficacy
Bibliographic record
Abstract
Background/Objectives: Health locus of control (LOC) refers to one’s perceptions of who or what controls one’s health. Recent evidence has found that chance LOC (CLOC) is associated with improved quality of life (QoL) in multiple sclerosis (MS). The purpose of the current study was to identify mediators and moderators of the LOC-QoL relationship in MS. Methods: For this study, 5266 participants with MS completed a questionnaire pack that included the Multidimensional Health Locus of Control Scale, the Unidimensional Self-Efficacy Scale for MS (USE-MS), and the World Health Organization Quality of Life Scale—BREF (WHOQoL-BREF). The relationship between LOC and QoL was examined within a structural equation model (SEM). Results: In the total sample, self-efficacy was found to fully mediate the relationship between LOC and QoL for both internal (ILOC) and CLOC orientations. Powerful others LOC (PLOC) had no association with QoL. The same results were found for the relationship of LOC to functioning. In the secondary progressive MS subgroup, the relationship between CLOC and QoL was only partially mediated by self-efficacy. Conclusions: LOC influences QoL through its impact on self-efficacy, one of several potentially mediating factors between LOC and QoL in MS. Disability did not moderate the associations of LOC, but moderation of the CLOC-QoL relationship by disease subtype was found. Psychological training to improve self-efficacy in MS may be particularly useful in those subgroups where LOC-QoL is largely mediated by self-efficacy.
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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".