Fibromyalgia: Predicting openness to counselling referrals
Bibliographic record
Abstract
Fibromyalgia (FM) is a chronic pain condition that can negatively impact on all aspects of patients' lives. The purpose of this study was: first, to explore the biopsychosocial factors that may contribute to adjustment to FM symptoms; second, to investigate how referrals to counselling related to patients' ratings of their relationship with their physicians; and, last, to examine if self-reports of illness distress, emotional problems, and practical problems can predict who will be open to counselling referrals. For this cross-sectional study, data from 190 people were collected through an online survey. Statistical analysis revealed that emotional problems reported were the best predictor of variance in illness distress and contentment scores. Further, results indicated that referrals made in accord with patients' perceptions that they would benefit from counselling may have a positive influence on how patients rate their relationships with their physicians. Finally, self-reported scores of illness distress, number of emotional problems, and number of practical problems accurately predicted who would be open to counselling referrals in 67% of cases. Research results provide support for addressing emotional issues to facilitate adjustment to FM symptoms, and for use of psychosocial measures to determine when patients with FM will be open to counselling referrals.
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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 | 0.002 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".