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Enregistrement W3181525081 · doi:10.1080/10401334.2021.1930545

Embedding Identity and How Clinical Teachers Reconcile Their Multiple Professional Identities to Meet Overlapping Demands at Work

2021· article· en· W3181525081 sur OpenAlexaff
David Ortiz-Paredes, Charo Rodríguez, Peter Nugus, Tamara E. Carver, Torsten Risør

Notice bibliographique

RevueTeaching and Learning in Medicine · 2021
Typearticle
Langueen
DomaineMedicine
ThématiqueInnovations in Medical Education
Établissements canadiensMcGill University Health CentreMcGill University
Organismes subventionnairesnon disponible
Mots-clésIdentity (music)EmbeddingWork (physics)Professional developmentMedical educationPsychologyPedagogySociologyPublic relationsMedicineComputer sciencePolitical scienceEngineeringPhilosophyAesthetics

Résumé

récupéré en direct d'OpenAlex

PHENOMENON: Clinical teachers perform overlapping tasks in education and patient care. They are therefore expected to juggle many professional identities such as educator and clinician. Yet little is known about how clinical teachers negotiate their professional identities. The present research examined the lived experiences of clinical teachers as they manage and make sense of their professional identities in the context of a faculty development program. APPROACH: This study adopted interpretative phenomenological analysis, which is an idiographic and inductive methodological approach that enables an in-depth examination of how people conceptualize their personal and social worlds. In-depth semi-structured individual interviews were conducted with six purposively sampled Brazilian clinical teachers who were attending a faculty development program. Each participant's lived experience was analyzed independently. Then, these individual analyses were compared against each other to identify convergence and divergence. FINDINGS: , containing other identities and roles. Participants integrated their professional identities in agreement with their personal identities, values, and beliefs, striving thus for identity consonance. Participants understood their craft as a relational process by which they wove themselves into their context and entangled their experience with that of others. They, however, diverged when recognizing who their peers were; whereas some named a single professional group (i.e., family physicians), others had a more comprehensive view and considered as peers healthcare professionals, students, and even patients. Finally, participants identified time constraints and lower prestige of family medicine as a medical discipline vis-à-vis other specialties as challenges posed by their contexts. INSIGHTS: Clinical teachers have multifaceted identities, to which they give a sense, manage, and integrate into their daily practice. Participants recognized an embedding identity and looked for common points between the identities it contained, which allowed them to meaningfully reconcile the different demands from their overlapping professional identities. Thus, this research introduces the notion of embedding identity as a strategy to make sense of many professional identities. Variability in the embedding identities depicted in this investigation suggests the fluid and contextualized character of professional identity development. How participants saw themselves also influenced how they behaved and interacted with others accordingly. Understanding clinical teacher identity development enriches current perspectives of what it is like to be one of these medical professionals. Faculty development programs ought to consider these perspectives to better support clinical teachers in meeting the overlapping demands in education and patient care.

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 distillée sur la base complète

Imitation des enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,007
score de la tête « metaresearch » (Gemma)0,029
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMétarecherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,065
Score d'incertitude au seuil0,979

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0070,029
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,002
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,055
Tête enseignante GPT0,405
Écart entre enseignants0,351 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2021
Routes d'admission1
Résumé présentoui

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