Meeting the Need for Human Connection in Our Health Care Workforce
Notice bibliographique
Résumé
To the Editor: Sustainable strategies to maintain human connection in our health care workforce are needed,1 especially amidst growing concerns about the potential for artificial intelligence (AI) to outperform human communication in areas that require genuine empathy and connection.2 These reports motivated us to share updates about our experience with an evidence-based experiential interprofessional medical improvisation program, the Alda Healthcare Experience (AHE), that focuses on building human connection, empathy, and team cohesion among our workforce. We believe approaches like this are critical to supporting health care team members’ resilience and ability to serve patients’ needs. The AHE is a series of brief workshops that draw upon the theater arts and the discipline of science communication to build health care communication skills with the explicit intent of promoting a positive organizational workforce culture. Others have demonstrated the positive impact of medical improvisational training on the ability to deliver messages effectively to colleagues and patients.3 The improvisational process—based on active listening, connectedness, and spontaneous collaboration with others—requires empathy and a willingness for an individual to adjust their own communication to meet another’s needs to build trust and foster a clear and accurate exchange of information. Effective team-based care and communication within and across health care organizations are well-established requirements to deliver safe, high-quality care. Supported by our organization’s leadership and the Health Resources and Services Administration, we are currently providing the AHE to 500 of our own health care professionals. Previously, our team was invited to deliver the AHE to health care professionals in partnership with other organizations (e.g., Gold Humanism Foundation, Planetree). Our team has delivered the AHE to health care professionals from the United States and Canada during a 2-day immersive medical improvisation training experience. We welcome the opportunity to partner with others to offer these workshops to their health care workforce to build a positive organizational culture that will enable all—patients and professionals alike—to thrive. Understanding that AI will play an evolving role in health care communication, we believe that AI should never fully replace human-to-human interactions. Leadership to create organizational cultures in health care that value and reward teamwork is urgently necessary. Susmita Pati, MD, MPHChief medical program advisor, Alan Alda Center for Communicating Science, professor of pediatrics and chief of the division of primary care pediatrics, Renaissance School of Medicine at Stony Brook University, Stony Brook, New York; email: [email protected]Laura Lindenfeld, PhDExecutive director, Alan Alda Center for Communicating Science, and dean, School of Communication and Journalism, Stony Brook University, Stony Brook, New YorkStacy Gropack, PT, PhDDean and professor, School of Health Professions, Stony Brook University, Stony Brook, New YorkHarold L. Paz, MD, MSProfessor of medicine, Renaissance School of Medicine, and former executive vice president for health sciences and chief executive officer, Stony Brook University Medicine, Stony Brook University, Stony Brook, New York
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,044 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,004 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,004 | 0,003 |
| Intégrité de la recherche | 0,018 | 0,026 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,004 |
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 source (Gemma direct ou Codex distillé), 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 ».