Barriers to Chronic Pain Measurement: A Qualitative Study of Patient Perspectives
Bibliographic record
Abstract
OBJECTIVE: Preliminary evidence suggests that chronic pain patients complete pain intensity measures using idiosyncratic methods. Our objective was to understand these methods and how they might impact the psychometric properties of the instruments. DESIGN: A qualitative focus-group based study. SETTING: An academic center in New York City. SUBJECTS: Outpatients (n = 36) with chronic low back pain, or neuropathic pain due to diabetes or HIV. METHODS: Participants were divided into three focus groups based on their pain condition, and asked to discuss pain intensity measures (visual analog and numeric rating scales for average pain over 24 hours; Brief Pain Inventory; and McGill Pain Questionnaire). Audio-recordings were transcribed and analyzed using an inductive thematic method. RESULTS: We discovered four main themes, and five sub-themes: 1) doubt that pain can be accurately measured (subthemes: pain measurement is influenced by things other than pain, the numbers used to rate pain do not have an absolute meaning, and preference for pain intensity ratings "in the middle" of the scale); 2) confusion regarding the definition of pain; 3) what experiences to use as referents (subthemes: appropriate comparator experiences and the interpretation of the anchors of the scale); and 4) difficulty averaging pain. CONCLUSIONS: The themes discovered suggest that patients include sensations and experiences other than pain intensity in their ratings, experience the rating of pain as a comparative task, and do not use the scale in a linear manner. These themes are relevant to understanding the validity and scale properties of commonly used pain intensity measures.
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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 | 0.034 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".