Latent structure of fear of pain: An empirical test among a sample of community dwelling older adults
Bibliographic record
Abstract
Recent findings from a sample of patients with acute and chronic musculoskeletal pain and headache indicate that fear of pain is characterized by latent continuity; that is, it is non-taxonic. It remains to be determined whether the latent structure of fear of pain is consistent between patients seeking treatment for pain versus those drawn from representative community samples. The purpose of the present investigation was to determine if the latent structure of fear of pain is characterized by latent continuity in a representative community sample of older adults. Using taxometric methods in a sample of 459 community dwelling older adults, we sought to evaluate the latent structure of fear of pain as indexed by the Pain Anxiety Symptoms Scale. Results from analyses of simulated Monte Carlo data, MAXEIG-HITMAX, and L-mode consistency tests indicated that the latent structure of fear of pain in this sample was the same as that previously reported in clinical samples, being non-taxonic and characterized by latent continuity. These findings confirm initial findings that fear of pain, at least as measured by the Pain Anxiety Symptoms Scale, is continuous, occurring along a latent continuum ranging from low to high. Results are discussed in relation to the conceptual understanding of fear of pain, implications for assessment and treatment, and future research directions.
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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 | 0.014 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| 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".