Seeing the Humanity in Health Care
Notice bibliographique
Résumé
The practice of medicine relies on the love of humanity. In the race for efficiency and innovation, physicians can often overlook the personal circumstances behind the data and presentation of the patient they are treating. Physicians should be focused on treating the patient, and not be driven purely by biomarkers, lab values, and cost efficiencies. When patient goals and priorities are neither valued nor sought by health practitioners, patients are more likely to suffer mental distress, especially those with chronic conditions. For example, depression and anxiety show a significant association with cardiovascular disease (CVD),[1] with an estimated 20.8% overall prevalence of depression in patients with CVD.[2] Patients with characteristics that are underrepresented in clinical studies, including those who are pregnant, of advanced age and with comorbidities, can fall through the data gaps in evidence-based care guidelines.[3,4] Person-centered care aims to treat THIS patient, not patients “like this.”[5] Health care also loses sight of the humanity of health workers. In one of the latest survey of US physicians, 45.2% of respondents reported at least one symptom of burnout compared with 62.8% in 2021 (which is likely due to the strain on the health care system caused by the COVID-19 pandemic), 38.2% in 2020, 43.9% in 2017, 54.4% in 2014, and 45.5% in 2011.[6] Burnout could pose risks to care quality, workforce stability, and patient safety. Aligning with the philosophy of Heart and Mind, HEALTH (WHO) is a state of complete physical, mental, and social well-being and not merely the absence of disease or infirmity, we are pleased to present this special issue. Randall Scott Stafford witnessed the humanity exhibited by a long-term patient in the Massachusetts General Hospital Coronary Care Unit, as detailed in “Jim Kane: Cardiomyopathy, Heart Failure, and the Energizer Bunny.” Jim endured multiple cancers, received several pacemakers, survived kidney failure with a transplant from his wife, and lost his left leg. Yet, he resumed an active life. Grace E. Kim discussed physician burnout from the perspective of a former resident. In “Impact of Sleep Loss in Residency – A Reflection,” Kim indicated health workers have the second highest percentage of sleep deprivation at 45%, even though the importance of sleep hygiene is stressed during medical training. The author suggests decreasing the patient-to-resident ratio and increasing rest times between shifts. “A Systematic Review of Medication Adherence Interventions for Patients with Heart Failure” was contributed by de Tantillo et al. They searched databases including CINAHL, PubMed, and Scopus and identified relevant intervention studies (n = 40). The inclusion criteria identified articles that are original reports of intervention studies exclusively in patients diagnosed with heart failure. They suggest clinicians should incorporate a multidimensional approach when promoting adherence to medication regimens, which includes patient-related factors (e.g., knowledge), socioeconomic factors (e.g., income), therapy-related factors (e.g., side effects), health care team and health system factors (e.g., communication with health care team), and condition-related factors (e.g., cognition). Among these, additional focus should be given to the health care team and health system factors. A study by Liu et al. titled “Advances in Cardiac Telerehabilitation for Older Adults in the Digital Age: A Narrative Review” provides an overview of the importance of cardiac telerehabilitation (CTR) related to issues of safety, efficacy, cost-effectiveness, and implementation in an effort to draw attention to such programs for older adults, enhance secondary prevention, and provide a reference basis for future users. The current high number of older adults with CVD, coupled with strained and limited medical resources, creates an urgent need for multiple cardiac rehabilitation modalities. The future design of CTR programs is expected to be more refined, standardized, richer in content, and easier to operate. An article titled “How Urban Design Science Can Reduce Stress: Current Understanding and Future Prospects” by Koohsari et al. presents a comprehensive framework on how urban design attributes can affect stress by modulating physiological responses. It also discusses current gaps and future directions on this topic. The paper concludes that some urban design attributes, such as walkability and availability of green spaces, may be associated with influencing stress and mental health in urban populations. The theme of this issue encompasses a diverse range of topics – such as the intersection of mental health and other diseases, the experiences of underrepresented groups, patient-centered design in health care environment, AI-driven medicine,[7] and therapy dogs.[8] Many important topics remain unexplored in this issue. Heart and Mind looks forward to publishing additional papers on humanity in health 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 | 0,019 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,009 | 0,031 |
| Communication savante | 0,020 | 0,023 |
| Science ouverte | 0,002 | 0,020 |
| Intégrité de la recherche | 0,012 | 0,025 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,032 | 0,007 |
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 ».