Measures of anxiety: is there a difference in their ability to predict functioning at three‐month follow‐up among pain patients?
Bibliographic record
Abstract
Independent investigators have found that pain is related to health anxiety, trait anxiety, pain-related anxiety, and anxiety sensitivity. To date, the relationship among these anxiety-related constructs has not been studied directly and little is known about their relative ability to predict adjustment to pain over time. This paper presents longitudinal data from measures given to 227 musculoskeletal pain patients. Patients were asked at the time of their first visit (T1) to a physiotherapy clinic to complete a questionnaire package including measures of these different forms of anxiety as well as pain severity, disability, negative affect, and perceived control. Approximately 3 months later (T2), 50% of patients responded to these same questionnaires. Results showed that correlations among the anxiety measures at T1 ranged from 0.35 to 0.56. Using multiple regression analyses, measures of T1 anxiety were each examined for their ability to predict unique variance in disability, negative affect, and perceptions of control measured at T1 and T2. At T1, after controlling for pain severity and other measures of anxiety, pain-related anxiety uniquely predicted both disability and negative affect, trait anxiety uniquely predicted negative affect and perceptions of control, and anxiety sensitivity uniquely predicted negative affect. At T2, after controlling for pain severity, other measures of anxiety and each respective measure of functioning at T1, health anxiety uniquely predicted disability and negative affect, although anxiety sensitivity also uniquely contributed to the prediction of negative affect. It is concluded that the importance of various forms of anxiety is dependent on the timeframe and outcome examined. Clinical implications of the findings as well as directions for future research are discussed.
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 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 | 0.013 | 0.005 |
| 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 | 0.000 |
| 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; a candidate call from one teacher head, 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".