Ethnocultural and Sex Characteristics of Patients Attending a Tertiary Care Pain Clinic in Toronto, Ontario
Bibliographic record
Abstract
BACKGROUND: Ethnocultural factors and sex may greatly affect pain perception and expression. Emerging literature is also documenting racial and ethnic differences in pain access and care. OBJECTIVE: To define the sex and ethnocultural characteristics of patients attending a tertiary care, university-affiliated pain clinic in Toronto, Ontario. METHODS: Data were collected on 1242 consecutive, new patients seen over a three-year period at the Comprehensive Pain Program (CPP) in downtown Toronto. Data were compared with the Canada 2001 Census. RESULTS: English-speaking, Canadian-born patients constituted 58.6% of the CPP population, similar to the 2001 Canadian Census data for the Greater Toronto Area. Certain visible minority groups (Indo-Pakistani and Chinese) were significantly under-represented, while European groups were over-represented. While women outnumbered men, they presented with lower levels of physical pathology in general, particularly in certain ethnic groups. Patients from Europe (representing primarily immigrants who arrived in Canada before 1960), were older, by 10 years to 15 years, than the average CPP population, and had a much higher incidence of physical or medical disorders. CONCLUSIONS: The implications of the study and the importance of sex and ethnicity in terms of presentation to Canadian pain clinics are discussed. Future well-designed studies are needed to shed light on the role of both patients' and physicians' ethnicity and sex in pain perception and expression, decision-making regarding pain treatments and acceptance of pain treatments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".