Heart failure management insights from primary care physicians and allied health care providers in Southwestern Ontario
Notice bibliographique
Résumé
BACKGROUND: It remains to be determined whether collaborative strategies to improve and sustain overall health in patients with heart failure (HF) are currently being adopted by health care professionals. We surveyed primary care physicians, nurses and allied health care professionals in Southwestern Ontario regarding how they currently manage HF patients and how they perceive limitations, barriers and challenges in achieving optimal management in these patients. METHODS: We developed an online survey based on field expertise and a review of pertinent literature in HF management. We analyzed quantitative data collected via an online questionnaire powered by Qualtrics®. The survey included 87 items, including multiple choice and free text questions. We collected participant demographic and educational background, and information relating to general clinical practice and specific to HF management. The survey was 25 min long and was administered in October and November of 2018. RESULTS: We included 118 health care professionals from network lists of affiliated physicians and clinics of the department of Family Medicine at Western University; 88.1% (n = 104) were physicians while 11.9% (n = 14) were identified as other health care professionals. Two-thirds of our respondents were females (n = 72) and nearly one-third were males (n = 38). The survey included mostly family physicians (n = 74) and family medicine residents (n = 25). Most respondents indicated co-managing their HF patients with other health care professionals, including cardiologists and internists. The vast majority of respondents reported preferring to manage their HF patients as part of a team rather than alone. As well, the majority respondents (n = 47) indicated being satisfied with the way they currently manage their HF patients; however, some indicated that practice set up and communication resources, followed by experience and education relating to HF guidelines, current drug therapy and medical management were important barriers to optimal management of HF patients. CONCLUSIONS: Most respondents indicated HF management was satisfactory, however, a minority did identify some areas for improvement (communication systems, work more collaborative as a team, education resources and access to specialists). Future research should consider these factors in developing strategies to enhance primary care involvement in co-management of HF patients, within collaborative and multidisciplinary systems of 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 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 | 0,001 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,004 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
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.
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 ».