Perspective of Pain Clinicians in Three Global Cities on Local Barriers to Providing Care for Chronic Noncancer Pain Patients
Bibliographic record
Abstract
An increasing proportion of the global chronic pain population is managed through services delivered by specialized pain clinics in global cities. This paper describes the results of a survey of pain clinic leaders in three global cities on barriers influencing chronic noncancer pain (CNCP) management provided by those clinics. It demonstrates a pragmatic qualitative approach for characterizing how the global city location of the clinic influences those results. A cross-sectional prospective survey design was used, and data were analyzed using quantitative and qualitative content analysis. Key informants were pain clinicians ( n = 4 women and 8 men) responsible for outputs of specialized pain clinics in academic hospital settings in three global cities: Toronto, Kuwait, and Karachi. Krippendorff’s thematic clustering technique was used to identify the repetitive themes in the data. All but one of the key informants had their primary pain training from Europe or North America. In Kuwait and Karachi, pain specialists were anesthesiologists and provided CNCP management services independently. In Toronto, pain clinic leaders were part of some form of the multidisciplinary team. Using the results of a question that asked informants to list their top three barriers, ten themes were identified. These themes were artificially organized in three thematic domains: infrastructure, clinical services, and education. In parallel, 31 predefined barriers identified from the literature were scored. The results showed variation in perception of barriers that not only depended on the clinic location but also demonstrated shared experiences across thematic domains. This study demonstrates a simple methodology for informing global and local efforts to improve access to and implementation of CNCP services globally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".