Perceived Self‐efficacy and Headache‐Related Disability
Bibliographic record
Abstract
BACKGROUND: Headache-specific self-efficacy refers to patients' confidence that they can take actions that prevent headache episodes or manage headache-related pain and disability. According to social cognitive theory, perceptions of self-efficacy influence an individual's adaptation to persistent headaches by influencing cognitive, affective, and physiological responses to headache episodes as well as the initiation and persistence of efforts to prevent headache episodes. OBJECTIVE: The objective of the present study was to construct and validate a brief measure of headache specific self-efficacy and to examine the relationship between self-efficacy and headache-related disability. METHODS: A sample of 329 patients seeking treatment for benign headache disorders completed the Headache Management Self-Efficacy Scale and measures of headache-specific locus of control, coping, psychological distress, and headache-related disability. A subset of 262 patients also completed 4 weeks of daily headache recordings. RESULTS: As predicted, patients who were confident they could prevent and manage their headaches also believed that the factors influencing their headaches were potentially within their control. In addition, self-efficacy scores were positively associated with the use of positive psychological coping strategies to both prevent and manage headache episodes and negatively associated with anxiety. Multiple regression analyses revealed that headache severity, locus-of-control beliefs, and self-efficacy beliefs each explained independent variance in headache-related disability.
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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 | 0.008 |
| 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) | 0.003 | 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".