International survey: real-world pain management strategies
Bibliographic record
Abstract
Abstract Background Pain is a nearly universal experience, but little is known about how people treat pain. This international survey assessed real-world pain management strategies. Methods From 13-31 January, 2020, an online survey funded by GSK Consumer Healthcare was conducted in local languages in Australia, Brazil, Canada, China, Colombia, France, Germany, India, Italy, Japan, Saudi Arabia, Malaysia, Mexico, Poland, Russia, Spain, Sweden, UK, and USA. Adults were recruited from online panels of people who agreed to participate in surveys. Quotas ensured nationally representative online populations based on age, gender, and region. Results Of 19,000 people (1000/country) who completed the survey, 18,602 (98%) had ever experienced physical pain; 76% said they would like to control their pain better. Presented with 17 pain-management strategies and asked to select the ones they use in the order of use, respondents chose an average of 4 strategies each. The most commonly selected strategies were pain medication (65%), rest/sleep (54%), consult a doctor (31%), physical therapy (31%), and nonpharmacologic action (eg, heat/cold application; 29%). Of those who use pain medication, 56% take some other action first. Only 36% of those who treat pain do so immediately; 56% first wait to see if it will resolve spontaneously. Top reasons for waiting include a desire to avoid medication (37%); willingness to tolerate less severe pain (33%); concerns about side effects (21%) or dependency (21%); and wanting to avoid a doctor's visit unless pain is severe or persistent (21%). Nearly half (42%) of those who take action to control pain have visited ≥1 healthcare professional (doctor 31%; pharmacist 18%; other 17%) about pain. Conclusions This large global survey shows that people employ a range of strategies to manage pain but still wish for better pain control. Although pain medication is the most commonly used strategy, many people postpone or avoid its use. Key messages More than three-quarters (76%) of respondents across countries seek better pain control. Pain medication and rest/sleep consultation are the most common pain management strategies. More than half of respondents (56%) wait to see if pain will resolve spontaneously before taking any action, and 56% of those who use pain medication try some other approach first.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".