Gender Representation of Health Care Professionals in Large Language Model–Generated Stories
Notice bibliographique
Résumé
Importance: With the growing use of large language models (LLMs) in education and health care settings, it is important to ensure that the information they generate is diverse and equitable, to avoid reinforcing or creating stereotypes that may influence the aspirations of upcoming generations. Objective: To evaluate the gender representation of LLM-generated stories involving medical doctors, surgeons, and nurses and to investigate the association of varying personality and professional seniority descriptors with the gender proportions for these professions. Design, Setting, and Participants: This is a cross-sectional simulation study of publicly accessible LLMs, accessed from December 2023 to January 2024. GPT-3.5-turbo and GPT-4 (OpenAI), Gemini-pro (Google), and Llama-2-70B-chat (Meta) were prompted to generate 500 stories featuring medical doctors, surgeons, and nurses for a total 6000 stories. A further 43 200 prompts were submitted to the LLMs containing varying descriptors of personality (agreeableness, neuroticism, extraversion, conscientiousness, and openness) and professional seniority. Main Outcomes and Measures: The primary outcome was the gender proportion (she/her vs he/him) within stories generated by LLMs about medical doctors, surgeons, and nurses, through analyzing the pronouns contained within the stories using χ2 analyses. The pronoun proportions for each health care profession were compared with US Census data by descriptive statistics and χ2 tests. Results: In the initial 6000 prompts submitted to the LLMs, 98% of nurses were referred to by she/her pronouns. The representation of she/her for medical doctors ranged from 50% to 84%, and that for surgeons ranged from 36% to 80%. In the 43 200 additional prompts containing personality and seniority descriptors, stories of medical doctors and surgeons with higher agreeableness, openness, and conscientiousness, as well as lower neuroticism, resulted in higher she/her (reduced he/him) representation. For several LLMs, stories focusing on senior medical doctors and surgeons were less likely to be she/her than stories focusing on junior medical doctors and surgeons. Conclusions and Relevance: This cross-sectional study highlights the need for LLM developers to update their tools for equitable and diverse gender representation in essential health care roles, including medical doctors, surgeons, and nurses. As LLMs become increasingly adopted throughout health care and education, continuous monitoring of these tools is needed to ensure that they reflect a diverse workforce, capable of serving society's needs effectively.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».