The Psychological Inflexibility in Pain Scale (PIPS) in Brazilian patients with chronic cancer pain: translation, cross-cultural adaptation, and validation study
Bibliographic record
Abstract
BACKGROUND: The Psychological Inflexibility in Pain Scale (PIPS) was developed to measure avoidance and cognitive fusion. OBJECTIVES: To translate, cross-culturally adapt, and analyze the measurement properties of the Psychological Inflexibility in Pain Scale (PIPS) in Brazilian patients with chronic cancer pain. METHODS: Questionnaire translation, cross-cultural adaptation, and validation studies were conducted in two hospitals in northeastern Brazil. The measurement properties tested included structural validity, construct validity, reliability, and internal consistency. The following assessment instruments were used in addition to the PIPS: Pain Catastrophizing Scale (PCS), Barthel Index, Edmonton Symptom Assessment Scale (ESAS), and Hospital Anxiety and Depression Scale (HADS). RESULTS: The study sample consisted of 122 patients, most of whom were women (65.6%) with a mean age of 49 years. Most patients had uterine cancer (23%) and leukemia (9.8%). We identified problems in the two-dimensional structure of the PIPS by presenting three inadequate fit indices. Adequate reliability was observed in both domains. Regarding the avoidance domain, there was a correlation with a magnitude > 0.30 with the depression domain of the HADS, and correlations with a magnitude < 0.30 with the anxiety domain of the HADS, the PCS domains, and the Barthel Index. The cognitive fusion domain did not correlate with any of these scales (P > 0.05). No ceiling or floor effects were observed. CONCLUSION: The Brazilian version of the PIPS is reliable; however, the instrument does not have a valid internal structure and the cognitive fusion domain is not a valid construct.
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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 | 0.004 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".