Development and validation of the Pain Resilience and Optimism Scale (PROS)
Bibliographic record
Abstract
Numerous self-report questionnaires have been used in pain research to explore patients' experiences. However, these questionnaires often employ negatively worded items that can potentially worsen patients' distress. In response to the emergence of positive psychology, this thesis aimed to develop a new questionnaire that adopts a positive and strengths-focused approach, incorporating resilience, to replace the negative items found in existing tools such as the Pain Catastrophizing Scale (PCS). First, the effectiveness of the Connor-Davidson Resilience Scale (CD-RISC) in measuring resilience following trauma was assessed through a systematic review using the COnsensus-based Standards for the selection of health status Measurement INstruments (COSMIN) checklist. The review revealed that the CD-RISC may not adequately capture resilience in the context of trauma. Consequently, a new tool called the Post-traumatic Resilience Scale was theorized and developed to address these limitations. In line with the potential benefits of positive psychological factors such as optimism in mitigating the effects of trauma, the 2nd and 3rd studies of this thesis aimed to explore these factors within the framework of Post-traumatic Resilience and Optimism (PTRO). In developing the initial items for the prototype Pain Resilience and Optimism Scale (PROS), researchers reversed the polarity of 13 items from the widely used PCS, transforming them into positively worded items. Feedback from three patients with chronic pain contributed to the creation of the 13-item test version of the PROS. Validation of the PROS involved a sample of Canadian military veterans with chronic pain. The refined version of the scale consisted of eight items categorized into two subfactors: Pain Optimism (5 items) and Pain Resilience (3 items). The reduction in items aligns with previous findings that a shorter version of the PCS adequately measures pain catastrophizing. In conclusion, this thesis proposes the PROS as a new measurement tool for research and clinical use. The validation analyses demonstrate promising psychometric properties, although further research is needed for replication. Incorporating advanced measurement models such as Item Response Theory may enhance the reliability and validity of the PROS in evaluating pain resilience and optimism.
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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 | 0.010 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".