Notice bibliographique
Résumé
To the Editor: We gladly read Eichbaum’s article,1 which we see as an invitation to integrate conflict into our understandings of interprofessional collaboration or teamwork in health care. We agree with the author that conflict is a fundamental aspect of care delivery, and that its creative potential needs to be better recognized; indeed, after noting the striking absence of power, conflict, and hierarchies in the interprofessional education literature, we made a similar call in 2015.2 We are thus grateful for Eichbaum’s identification of three ways forward for collaboration and teamwork, but would like to stress how the proposed solutions often clash with the context and complexities of care. First, while Eichbaum notes that not all collaborative work is done within teams, he does not tell us how psychological safety, innovation, or the health humanities might help individual workers collaborate safely or innovate when there is no “team” to speak of. In the context of teaching hospitals, where trainees and academic faculty regularly move on and off clinical services, this is a core problem. Finding a way to conceptualize these shape-shifting “teams” as collaborative entities will be essential if we are to develop effective and appropriate education for collaboration. Turning to frameworks such as Hollenbeck and colleagues’3—who define teams by their skill differentiation, authority differentiation, and temporal stability—might be a starting point. Moreover, the use of Steve Jobs to illustrate the importance of collaborative intelligence and the associated trade-offs between agreeableness (of which Jobs had none) and creative nonconformism raises fascinating yet unanswered questions: Where is the line between acceptable and unacceptable behavior? Where does potentially creative conflict stop and destructive rudeness begin? Furthermore, while Eichbaum emphasizes how evaluative systems hinder interpersonal risk taking—including behavior that might seem “unprofessional,” such as speaking up and pushing back—he offers no insight into how we might change these systems to encourage productive conflict and innovation. While solutions to this thorny issue may seem elusive, it is imperative that as an academic community we tackle such concerns head-on, examining both theoretically and empirically how to support innovation and risk taking in these settings. Finally, while we absolutely support the goal of flattening care hierarchies, we doubt that educational interventions alone will suffice here, as we have argued elsewhere.2,4 In sum, we applaud Eichbaum’s piece, and invite our community to tackle the very hard problems of teamwork and collaboration he has raised by confronting, directly, their complexities. Elise Paradis, MA, PhDAssistant professor, Leslie Dan Faculty of Pharmacy, Department of Anesthesia, and Department of Sociology, University of Toronto, and scientist, Wilson Centre, Toronto, Ontario, Canada; [email protected] Cynthia R. Whitehead, MD, PhDAssociate professor, Department of Community and Family Medicine, University of Toronto, director and scientist, Wilson Centre, and vice president for education, Women’s College Hospital, Toronto, Ontario, Canada.
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 | 0,008 | 0,050 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,012 |
| Communication savante | 0,011 | 0,012 |
| Science ouverte | 0,006 | 0,005 |
| Intégrité de la recherche | 0,023 | 0,052 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,002 |
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 ».