Sex and gender differences in healthcare utilisation trajectories: a cohort study among Quebec workers living with chronic pain
Bibliographic record
Abstract
OBJECTIVES: Chronic pain (CP) is a poorly recognised and frequently inadequately treated condition affecting one in five adults. Reflecting on sociodemographic disparities as barriers to CP care in Canada was recently established as a federal priority. The objective of this study was to assess sex and gender differences in healthcare utilisation trajectories among workers living with CP. DESIGN: Retrospective cohort study. PARTICIPANTS: This study was conducted using the TorSaDE Cohort which links the 2007-2016 Canadian Community Health Surveys and Quebec administrative databases (longitudinal claims). Among 2955 workers living with CP, the annual number of healthcare contacts was computed during the 3 years after survey completion. OUTCOME: Group-based trajectory modelling was used to identify subgroups of individuals with similar patterns of healthcare utilisation over time (healthcare utilisation trajectories). RESULTS: Across the study population, three distinct 3-year healthcare utilisation trajectories were found: (1) low healthcare users (59.9%), (2) moderate healthcare users (33.6%) and (3) heavy healthcare users (6.4%). Sex and gender differences were found in the number of distinct trajectories and the stability of the number of healthcare contacts over time. Multivariable analysis revealed that independent of other sociodemographic characteristics and severity of health condition, sex-but not gender-was associated with the heavy healthcare utilisation longitudinal trajectory (with females showing a greater likelihood; OR 2.6, 95% CI 1.6 to 4.1). CONCLUSIONS: Our results underline the importance of assessing sex-based disparities in help-seeking behaviours, access to healthcare and resource utilisation among persons living with CP.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".