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Enregistrement W2060942595 · doi:10.1111/j.1365-2929.2007.02752.x

Physician, heal thyself: tools for resident well‐being

2007· article· en· W2060942595 sur OpenAlexaff
Jo Marie Reilly, Jeffrey M. Ring

Notice bibliographique

RevueMedical Education · 2007
Typearticle
Langueen
DomaineHealth Professions
ThématiqueHealthcare professionals’ stress and burnout
Établissements canadiensLondon Health Sciences Centre
Organismes subventionnairesnon disponible
Mots-clésInternshipMedical educationContext (archaeology)ConversationHealth carePsychologyChecklistMedicineNursing

Résumé

récupéré en direct d'OpenAlex

Context and setting We have implemented an introductory workshop during internship orientation to introduce the concept of doctors as healers of themselves and their patients. Why the idea was necessary Medical training is incredibly stressful. Little attention is given to training doctors in self-care. We believe that teaching residents strategies for incorporating personal well-being into their daily lives is essential for longterm doctor wellness and should be part of residents' professional development. What was done We initiated an introductory workshop during orientation, co-facilitated by a doctor and a behaviourist faculty member, on the doctor's role as self-healer. The workshop serves as a catalyst for future discussions on self-care during residency training and plants seeds for personal well-being early on in the training process. First, the interns fill out the health awareness checklist that is given to clinic patients during physical examinations. Anonymously completed and self-reviewed, their answers stimulate a conversation about their personal self-care practices in personal nutrition and exercise, substance use and abuse, sexual health and safety, and overall emotional health. Together, we explore the notion that doctors who are more aware of their own health vulnerabilities and needs are more successful as healers of others. Next, the interns receive a pedometer as a personal fitness tool and are asked to record their steps for 1 month. The ‘step sheet’ is submitted to the faculty and tabulated each week during the first month of residency training. Consistent with the ‘Walk Across America Program’, the intern classes' total steps are calculated and marked with a shoe that indicates their collective progress towards personal fitness along a map of the USA. Wearing their pedometers in clinic serves both to model fitness to patients and to remind and motivate themselves to stay active and take care of themselves. Finally, the interns complete a personal wellness prescription. Here they list 3 commitments to themselves that will improve or maintain their physical and emotional well-being during residency training. These are made confidentially, sealed in envelopes and distributed to the intern's faculty advisor for further discussion. They are asked to re-visit their wellness prescription with their advisor on a bi-annual basis and modify it accordingly. Evaluation of results and impact Three intern classes (n = 21) have now completed the workshop on the doctor's role as self-healer. The data collected have been qualitative and highly positive. The most consistent feedback indicates that the interns feel valued and affirmed that faculty staff ‘care about them’ as individuals and doctor healers, independent of their clinical performance. Many appreciate the focus on raising their own self-care awareness, particularly at a very stressful time in their medical training. They appreciate the wellness prescriptions and their meetings with their advisors to check in on their self-care plan, feeling that this keeps them accountable for their well-being plans. Finally, the residents appreciate the pedometers as tools and reminders of personal fitness, especially as they have entered a training programme where consistent exercise is difficult to achieve. Some commented how patient inquiries about the pedometer have opened discussions about the importance of exercise.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,014
score de la tête « metaresearch » (Gemma)0,025
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Théorique ou conceptuel · Signal consensuel: aucune
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,073

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0140,025
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0020,001
Études des sciences et des technologies0,0020,002
Communication savante0,0040,004
Science ouverte0,0010,006
Intégrité de la recherche0,0010,002
Charge utile insuffisante (le modèle a refusé de juger)0,0060,001

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 Opus0,054
Tête enseignante GPT0,495
Écart entre enseignants0,441 · 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 source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeThéorique ou conceptuel
Domainenon disponible
GenreCommentaire

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

En bref

Citations4
Publié2007
Routes d'admission1
Résumé présentoui

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