Topical Review: Acute and Chronic Pain Experiences in Transgender and Gender-Diverse Youth
Bibliographic record
Abstract
OBJECTIVE: To provide an overview of the existing literature on gender diversity in pediatric acute and chronic pain, propose an ecological systems model of understanding pain in transgender and gender-diverse (TGD) youth, and identify a direction for future work that will address the key knowledge gaps identified. METHODS: Relevant literature on pain and gender diversity was reviewed, drawing from adult literature where there was insufficient evidence in pediatric populations. Existing relevant models for understanding minority stress, gender and pain, and pain experiences within marginalized groups were considered with the reviewed literature to develop a pain model in TGD youth. RESULTS: While there is an abundance of literature pointing to increased risk for pain experiences amongst TGD youth, there is comparably little empirical evidence of the rates of pain amongst TGD youth, prevalence of TGD identities in pain care settings, effective pain treatments for TGD youth and unique considerations for their care, and the role intersectional factors in understanding TGD youth identities and pain. CONCLUSION: Pediatric psychologists are well-positioned to advance the research on acute and chronic pain in TGD youth, make evidence-based adaptations to clinical care for TGD youth with pain, including pain related to gender affirmation, and support colleagues within the medical system to provide more inclusive care.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".