Predicting Readiness to Self-manage Pain
Bibliographic record
Abstract
OBJECTIVE: The goal of multidisciplinary treatment for chronic pain is to help patients actively self-manage pain. In this study, we examined predictors of 2 measures of readiness to self-manage pain, namely the Precontemplation and Action subscales of the Pain Stages of Change Questionnaire. In particular, we examined the relative importance of experiences with pain and the primary care physician and beliefs about self-efficacy and pain control in predicting intention to self-manage pain (Precontemplation) and actual use of pain self-management strategies (Action). METHOD: One hundred and two chronic pain participants, from 4 multidisciplinary rehabilitation centers, completed the Precontemplation and Action subscales. They also completed self-report questionnaires assessing pain severity, interference, depression, pain-related anxiety, perceptions of the patient-physician relationship, pain locus of control beliefs, and pain self-efficacy. RESULTS: Considerable variance in Precontemplation scores (49%) was explained by the variables studied. Beliefs about powerful others controlling pain and perceptions of low internal control were particularly salient in the prediction of Precontemplation scores. Less variance was explained in Action scores (35%). Satisfaction with information provided by the physician was uniquely related to Action scores. DISCUSSION: The results of the study are placed within the context of the Motivational Model of Pain Self-Management and provide insight into factors that are associated with motivation to self-manage pain. Future directions for research are discussed with respect to perceptions of pain control and satisfaction with information from physicians, constructs which have previously been overlooked in research on motivation to self-manage pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".