The chronic disease helplessness survey: developing and validating a better measure of helplessness for chronic conditions
Bibliographic record
Abstract
Introduction: Learned helplessness develops with prolonged exposure to uncontrollable stressors and is therefore germane to individuals living with pain or other poorly controlled chronic diseases. This study has developed a helplessness scale for chronic conditions distinct from previous scales that blur the conceptualization of control constructs. Extant measures commonly examine controllability, not the three pillars of helplessness identified by Maier and Seligman (1976): cognitive, emotional, and motivational/motor deficits. Methods: Individuals who self-report a chronic pain condition (N = 350) responded to a Chronic Disease Helplessness Survey (CDHS) constructed to capture cognitive, motivational/motor, and emotion deficits. Exploratory factor analysis (EFA; N = 200) and confirmatory factor analysis (CFA; N = 150) were performed. The CDHS was assessed for convergent and discriminant validity. Results: A three-factor solution corresponding to cognitive, emotional, and motivational/motor factors was identified by EFA. The solution exhibited sufficient model fit and each factor had a high degree of internal consistency. The CDHS was significantly associated with greater pain intensity and interference, PCS helplessness, lower perceived pain control, and lower general self-efficacy. Individuals with diabetes generally experience greater control strategies over daily symptoms (e.g., diet, oral medications, and insulin) than patients with chronic pain and in this study displayed significantly lower CDHS scores compared to individuals with chronic pain, demonstrating discriminant validity. Conclusions: This study provides preliminary evidence that the three-factor CDHS is a psychometrically sound measure of helplessness in individuals with chronic pain.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".