A sex- and gender-based analysis plus of frequent healthcare utilization among individuals living with chronic pain: a cohort study
Bibliographic record
Abstract
Abstract Background Chronic pain (CP) affects up to 1 in 4 individuals and disproportionately impacts women, gender minorities, and other equity-deserving groups, highlighting the need for an equity-oriented approach to provide optimal care. The relationship between sex, gender, and frequent healthcare utilization remains underexplored among individuals living with CP. Therefore, this study aimed to examine the associations between sex, gender and frequent medical care utilization among persons living with CP. Methods The COPE Cohort, composed of persons living with CP, was formed by linking a web-based questionnaire with health administrative databases. Frequent medical care users were defined as the top 10% of our sample with the highest number of all-cause medical visits in the year following the completion of the questionnaire (including outpatient and emergency department visits). Sex (male / female), gender identity (men / women / gender-diverse), and gender-stereotyped personality traits (masculine / feminine / androgynous / undifferentiated; Bem Sex-Role Inventory) were analyzed. Cluster analysis was used to create intersecting sociodemographic subgroups (incorporating sex, gender, region of residence, country of birth, education level, employment status, and age). Results Among 895 participants, 95 were classified as frequent medical care users (10% cut-off: ≥13 visits/year). The proportion of frequent users varied across sex (male: 3.9% vs. female: 12.0%, p = 0.003) and gender identity (men: 4.0% vs. women: 12.1% vs. gender-diverse: 0%, p = 0.009), but not by gender-stereotyped personality traits subgroups. Multivariable logistic regression showed that the subgroup labelled ‘unemployed more educated women’ (compared to ‘men’) had increased odds of being frequent medical care users (OR: 3.93, 95%CI: 1.44–10.80). Conclusion Based on our results, we emphasize the need for clinicians and decision-makers to adopt an integrated approach that considers not only clinical, but importantly, socioeconomic profiles for effective healthcare.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.023 | 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".