Prioritising tool(s) for measuring chronic pain-related outcomes as part of the pharmacotherapy service in Scotland: a patient consensus study
Bibliographic record
Abstract
BACKGROUND: Pharmacist-led pharmacotherapy services in Scotland play a crucial role in managing people's chronic pain. Exploring patient preferences regarding pain-specific and quality of life assessment tools is essential for delivering patient-centred care. However, the current evidence base remains limited. OBJECTIVES: To obtain patient consensus on their preferred tools appropriate for measuring chronic pain outcomes as part of the pharmacist-led pharmacotherapy service in Scotland; and to obtain patient perspectives on the interpretability, comprehensibility and accessibility of tool(s). METHODS: A predominantly convenience sampling strategy recruited patients with experience of chronic pain in Scotland. An online modified nominal group technique (NGT) was conducted. This involved reviewing five outcomes tools, round robin feedback, discussions, ranking, and a final consensus exercise. Ranking results were used to identify the most preferable tool for use in practice. Discussions throughout the NGT were thematically analysed to explore the comprehensibility, interpretability, and accessibility of the tools. RESULT: Seven patients participated, from 6 of 14 Scottish health boards. Consensus identified the Brief Pain Inventory as the most preferable tool, with the EQ-5D-5L and Short-Form McGill Pain Questionnaire ranked joint second. Thematic analysis highlighted key areas for consideration related to tool comprehensibility and fundamental concepts surrounding the definition of pain and the subjectivity of the language used in tools. General comments included the importance of digital accessibility, and preferences around pain-specific vs HRQoL tools. CONCLUSIONS: The findings will facilitate decision making regarding the delivery of pharmacotherapy chronic pain services in Scotland. There is a preference for a disease-specific tool, however a combination of the BPI and the more general EQ-5D-5L could be used to capture more insight into the impact of the service on patients. Future work should test these tools in diverse populations and explore the use of the EQ-5D-5L with condition-specific tools for patients with various chronic conditions.
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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 | 0.169 | 0.233 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.002 |
| 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".