Bibliographic record
Abstract
Transgender care amongst youth has received media and political attention in recent years, with broad impacts on transgender patients, especially in the field of pediatrics. Barriers to accessing transgender health care has broad implications on health outcomes such as suicidality and should be prioritized by all healthcare professionals. We discuss strategies for all providers to ensure equitable access and highlight some important considerations for medical trainees and early career physicians. ---------- Les soins transgenres chez les jeunes ont retenu l’attention des médias et des politiques ces dernières années, ce qui a eu des vastes répercussions sur les patients transgenres, en particulier dans le domaine de la pédiatrie. Les obstacles au soins contribuent à de mauvais résultats, y compris l’augmentation de la suicidalité, soulignant la nécessité d’un accès équitable aux soins de santé pour les personnes transgenres. Cet article présente des stratégies permettant aux prestataires de soins de santé de surmonter ces obstacles et souligne les points importants à prendre en compte par les stagaires en médecine et les médecins en début de carrière.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 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".