Building Compassionate Experience Through Compassionate Action: Qualitative Behavioral Analysis
Notice bibliographique
Résumé
BACKGROUND: The acceleration of technology-based primary care during the COVID-19 pandemic outpaced the ability to understand whether and how it impacts care delivery and outcomes. As technology-based care continues to evolve, focusing on the core construct of compassion in a primary care context will help ensure high-quality patient care and increased patient autonomy and satisfaction. The ability to successfully operationalize the use of technology in patient-clinician interactions hinges on understanding not only how compassionate care is experienced in this context but also how clinicians can create it. OBJECTIVE: The objectives of this study were to understand whether and how compassionate behaviors are experienced in technology-based primary care interactions and identify the individual and contextual drivers that influence whether and how these behaviors occur. METHODS: We conducted a series of qualitative one-on-one interviews with primary care physicians, nurses, and patients. Qualitative data were initially analyzed using an inductive thematic analysis approach to identify preliminary themes for each participant group independently. We then looked across participant groups to identify areas of alignment and distinction. Descriptions of key behaviors that participants identified as elements of a compassionate interaction and descriptions of key drivers of these behaviors were inductively coded and defined at this stage. RESULTS: A total of 74 interviews were conducted with 40 patients, 20 nurses, and 14 primary care physicians. Key behaviors that amplified the experience of compassion included asking the patient's modality preference, using video to establish technology-based presence, sharing the screen, and practicing effective communication. Participants' knowledge or skills as well as their beliefs and emotions influenced whether or not these behaviors occurred. Contextual elements beyond participants' control influenced technology-based interactions, including resource access, funding structures, culture, regulatory standards, work structure, societal influence, and patient characteristics and needs. A high-yield, evidence-based approach to address the identified drivers of compassion-focused clinician behavior includes a combination of education, training, and enablement. CONCLUSIONS: Much of the patient experience is influenced by clinician behavior; however, clinicians need a supportive system and adequate supports to evolve new ways of working to create the experience of compassionate care. The current state of technology-based care operationalization has led to widespread burnout, societal pressure, and shifting expectations of both clinicians and the health system more broadly, threatening the ability to deliver compassionate care. For clinicians to exhibit compassionate behaviors, they need more than just adequate supports; they also need to receive compassion from and experience the humanity of their patients.
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 | 0,023 | 0,028 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,008 | 0,009 |
| Communication savante | 0,004 | 0,004 |
| Science ouverte | 0,002 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| 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 ».