Nature, Nurture, or Both? Study of sex and gender and their effects on pain
Bibliographic record
Abstract
As a pain researcher, in order to have a better understanding of pain, we should adopt a multidimensional view, such as the biopsychosocial (BPS) model and consider physical, psychological, and social elements altogether. The studies in this dissertation are part of the bigger project of SYMBIOME in which the aim is to help to create and develop a prognostic clinical phenotype in people post musculoskeletal (MSK) trauma. Chapter 2 presents a Confirmatory Factor Analysis (CFA) in order to assess the structural validity of the first section of the new Gender Pain and Expectation Scale (GPES). Our analysis indicated a 3-factor structure “Relationship-oriented,” “Emotive” and “Goal-oriented”. Its construct validity was also assessed. The subsequent study, chapter 3, explores the roles of sex-at-birth, GPES subscales and their interactions in explaining the variability of Brief Pain Inventory (BPI) Severity and Interference scores. It showed no sex differences in scores of BPI Pain Severity and BPI Pain Interference. GPES Relationship-oriented had a significant association with BPI pain severity (r=0.20) while GPES Emotive had a significant correlation with BPI Interference (r=0.24). Also, hierarchical multivariate linear regression suggested that GPES Emotive could partially explain the variances in pain-related interference. Chapter 4 presents correlations between sex-at-birth, hormones (Progesterone, DHEA-S, Estradiol, and Testosterone), GPES subscales and BPI scores. Also, as our second goal of this chapter, potential pathways between these variables have been tested through structural equation modelling. It has been shown that GPES Relationship-oriented had a significant correlation with progesterone (r=-0.21) and DHEA-S (r=-0.33), and GPES Emotive had a significant correlation with the DHEA-S (r=-0.20). The GPES Goal-oriented had a significant association with estradiol (r=-0.20). Our findings suggest that gender-related interpersonal-expressive characteristics could have mediator roles in relationships between sex-at-birth and pain, and also between the hormones and BPI pain ratings.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".