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Enregistrement W2376328541 · doi:10.4103/2347-5625.164999

Achieving Excellence in Palliative Care: Perspectives of Health Care Professionals

2015· review· en· W2376328541 sur OpenAlexaff
Margaret I. Fitch, Tracey DasGupta, Bill Ford

Notice bibliographique

RevueAsia-Pacific Journal of Oncology Nursing · 2015
Typereview
Langueen
DomaineMedicine
ThématiquePalliative Care and End-of-Life Issues
Établissements canadiensSunnybrook Health Science CentreUniversity of Toronto
Organismes subventionnairesnon disponible
Mots-clésPalliative careDebriefingNursingExcellenceFocus groupEnd-of-life careHealth careMedicinePsychologyMedical education

Résumé

récupéré en direct d'OpenAlex

Caring for individuals at the end of life in the hospital environment is a challenging proposition. Understanding the challenges to provide quality end of life care is an important first step in order to develop appropriate approaches to support and educate staff members and facilitate their capacity remaining "caring." Four studies were undertaken at our facility to increase our understanding about the challenges health professionals experience in caring for patients at end of life and how staff members could be supported in providing care to patients and families: (1) In-depth interviews were used with cancer nurses (n = 30) to explore the challenges talking about death and dying with patients and families; (2) Surveys were used with nurses (n = 27) and radiation therapists (n = 30) to measure quality of work life; (3) and interprofessional focus groups were used to explore what it means "to care" (five groups held); and (4) interprofessional focus groups were held to understand what "support strategies for staff" ought to look like (six groups held). In all cases, staff members confirmed that interactions concerning death and dying are challenging. Lack of preparation (knowledge and skill in palliative care) and lack of support from managers and colleagues are significant barriers. Key strategies staff members thought would be helpful included: (1) Ensuring all team members were communicating and following the same plan of care, (2) providing skill-based education on palliative care, and (3) facilitating "debriefing" opportunities (either one-on-one or in a group). For staff to be able to continue caring for patients at the end of life with compassion and sensitivity, they need to be adequately prepared and supported appropriately. Caring for individuals at the end of life in the hospital environment is a challenging proposition. Understanding the challenges to provide quality end of life care is an important first step in order to develop appropriate approaches to support and educate staff members and facilitate their capacity remaining "caring." Four studies were undertaken at our facility to increase our understanding about the challenges health professionals experience in caring for patients at end of life and how staff members could be supported in providing care to patients and families: (1) In-depth interviews were used with cancer nurses (n = 30) to explore the challenges talking about death and dying with patients and families; (2) Surveys were used with nurses (n = 27) and radiation therapists (n = 30) to measure quality of work life; (3) and interprofessional focus groups were used to explore what it means "to care" (five groups held); and (4) interprofessional focus groups were held to understand what "support strategies for staff" ought to look like (six groups held). In all cases, staff members confirmed that interactions concerning death and dying are challenging. Lack of preparation (knowledge and skill in palliative care) and lack of support from managers and colleagues are significant barriers. Key strategies staff members thought would be helpful included: (1) Ensuring all team members were communicating and following the same plan of care, (2) providing skill-based education on palliative care, and (3) facilitating "debriefing" opportunities (either one-on-one or in a group). For staff to be able to continue caring for patients at the end of life with compassion and sensitivity, they need to be adequately prepared and supported appropriately.

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)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Autre devis · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,792
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0050,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0010,002
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 Opus0,234
Tête enseignante GPT0,555
Écart entre enseignants0,321 · 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'étudeAutre devis
Domainenon disponible
GenreSynthèse

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

En bref

Citations15
Publié2015
Routes d'admission1
Résumé présentoui

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