A developmental framework for understanding the influence of sex and gender on health: Pediatric pain as an exemplar
Bibliographic record
Abstract
Sex differences are a robust finding in many areas of adult health, including cardiovascular disease, psychiatric disorders, and chronic pain. However, many sex differences are not consistently observed until after the onset of puberty. This has led to the hypothesis that hormones are primary contributors to sex differences in health outcomes, largely ignoring the relative contributions of early developmental influences, emerging psychosocial factors, gender, and the interaction between these variables. In this paper, we argue that a comprehensive understanding of sex and gender contributions to health outcomes should start as early as conception and take an iterative biopsychosocial-developmental perspective that considers intersecting social positions. We present a conceptual framework, informed by a review of the literature in basic, clinical, and social science that captures how critical developmental stages for both sex and gender can affect children's health and longer-term outcomes. The literature on pediatric chronic pain is used as a worked example of how the framework can be applied to understanding different chronic conditions.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".