Providing for Providers: Peer Support Groups for Psychologists and Trainees in Pediatric Gender Health
Bibliographic record
Abstract
Objective: In response to increasing anti-trans legislation targeting access to evidence-based gender-affirming healthcare in many states, the Gender Health Special Interest Group (SIG) of the Society of Pediatric Psychology started offering peer support groups for pediatric psychologists and trainees in the field of pediatric gender health. This commentary describes the impact of anti-trans legislation on the provision of pediatric gender-affirming healthcare and outlines the peer support group model, key lessons learned, and future directions for the groups. Methods: Peer support groups occurred 1–2 times monthly for pediatric psychologists, were facilitated by SIG leadership, and focused on information sharing, case consultation, and processing of the anti-trans sociopolitical climate. Trainee support groups occurred monthly, primarily for psychology trainees currently working in or interested in pediatric gender health. These were facilitated by the SIG student representatives, followed a loose structure bounded by group guidelines, and discussed a range of topics. Results: The groups drew variable, but modest, attendance. They offered a safe space for members to process the challenges of providing care in a tenuous sociopolitical climate. Logistical challenges (e.g., accommodating a variety of time availabilities) continue to be an area of growth for the groups. Conclusions: Peer support groups are a promising model for offering support, resources, and solidarity to pediatric psychologists providing care in controversial and politicized areas of healthcare. Healthcare institutions would benefit from considering ways to reduce barriers for pediatric psychologists and trainees attending support groups (e.g., counting attendance towards clinical productivity or continuing education).
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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 | 0.009 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".