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Enregistrement W4388222826 · doi:10.1002/jhm.13232

Combating weight bias

2023· article· en· W4388222826 sur OpenAlexaboutno aff
Don Mathew

Notice bibliographique

RevueJournal of Hospital Medicine · 2023
Typearticle
Langueen
DomaineHealth Professions
ThématiqueObesity and Health Practices
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésMedicineMEDLINELaw

Résumé

récupéré en direct d'OpenAlex

Obesity medicine is largely viewed as an outpatient specialty. So, when I, a hospitalist, obtained certification in obesity medicine, I faced numerous questions from my colleagues on my decision to pursue this field and what I was trying to accomplish as a dual-board-certified hospitalist. Obesity is defined as a chronic, relapsing, multifactorial, neurobehavioral disease, wherein an increase in body fat promotes adipose tissue dysfunction and abnormal fat mass physical forces, resulting in adverse metabolic, biomechanical, and psychosocial health consequences. Obesity is related to genetic, psychological, physical, metabolic, neurological, and hormonal impairments.1 It was during my preparation for the obesity medicine boards that I was introduced to an underdiscussed consequence of obesity: weight bias. Weight biases include assumptions that people living with obesity are lazy, incompetent, lacking willpower, and self-discipline, and not motivated to improve their health.2, 3 About 42% of adults with body mass indices (BMI) ≥35 have reported experiencing weight bias.4 People who have experienced weight bias are at risk for low self-esteem, depression, lower quality of life, and increased suicidality.5 Individuals who experienced weight bias had higher levels of C-reactive protein, cortisol, long-term cardiometabolic risk, and increased mortality, irrespective of baseline BMI. Weight bias has been reported in multiple societal domains, including the workplace, educational settings, healthcare settings, and within families.6 A survey of 359,261 people demonstrated that antiobesity bias is as prevalent among physicians as in the general public.7 Individuals who have experienced or perceived weight bias may delay or even avoid seeking treatment for medical problems for fear of stigmatization.8 It is, therefore, imperative that we realize the pervasive existence of this bias and take adequate measures to address it. Many patients with obesity lack regular follow-ups in the outpatient setting, resulting in their first interaction with a physician occurring during hospital admissions for medical problems or placement.8 Therefore, hospitalists have a unique opportunity to bridge this gap, providing compassionate and unbiased care, fostering a safe environment that encourages all patients to seek the medical attention they deserve and thereby playing a crucial role in combating weight bias. Negative interactions with physicians are often based on the lack of awareness around the causality and controllability of higher weight. Patients have described a lack of individualized care and prescription of overly simplistic lifestyle interventions as treatments for obesity.9 Reframing the narrative to characterize obesity as a chronic condition in which weight is not a behavior may reduce internalized weight bias and improve perceived patient–provider relationships.10 Patients with obesity perceive lower quality of care in inpatient settings.9, 11 Such perceptions could be derived from verbal and nonverbal cues, use of disrespectful language, decreased eye contact, unwillingness to touch patients, and dismissal of nonweight-related concerns.9 Anchoring bias can also play a critical role in the care of patients with obesity. Anchoring bias occurs when a physician focuses on a single, often initial, piece of information when formulating a diagnosis without sufficiently adjusting to later information.12 Consequently, this bias can result in the oversight of other crucial health factors, delayed or inaccurate diagnoses, and inadequate treatment plans. The widely circulated story of Ms. Ellen Bennett, a 64-year-old Canadian woman whose symptoms were dismissed as weight-related only to be diagnosed with advanced cancer days before her death, is a tragic testament to the risks that patients face when doctors fail to look past a patient's weight.13 There are several opportunities for hospitalists to reduce the prevalence of weight bias in clinical interactions in ways that promote high-quality and trusted patient care. First, obesity is poorly documented by hospitalists.14 Identifying obesity as a medical condition in the problem list and formulating a comprehensive strategy for its management would prevent hospitalists from unintentionally neglecting its significance. Second is the use of respectful verbiage. The Obesity Society recommends the use of “people first” language. Therefore, it is recommended to use “person with obesity” rather than “obese patients.”15 Also, the terms “morbidly obese” and “fat” are stigmatizing.16 The term “morbidly obese” should be substituted with “class III obesity” which is typically used to describe patients with BMI ≥40.17 In an age where patients can access medical records, the use of “people first” language can go a long way in re-establishing trust. It is also important that hospitals have adequate equipment to care for patients with obesity. Patients with obesity may require specialized equipment to accommodate their body size and weight, such as larger beds, wider wheelchairs, appropriate-sized blood pressure cuffs, and sufficient staff available to care for them. Insufficient equipment and staff can lead to discomfort, compromised care, and potential safety risks for patients.18 Addressing anchoring biases is more challenging. Ideally, clinicians would not rush to attribute patient symptoms solely to obesity but instead conduct a thorough workup just like any other patient. But while weight bias is well described in the literature, research on weight bias reduction interventions is limited. Prior work has focused mainly on educating clinicians or those in training on the causes and controllability of obesity and/or creating awareness of weight bias through patient stories or simulation exercises. The results of these studies have been mixed but overall showed a decrease in weight bias after participating in weight bias reduction interventions.19 There is some data that sustained reductions in weight bias postintervention may not last over time, but the evidence is limited due to the limited number of studies.20 It is also important to recognize that most hospitalized patients with obesity welcome inpatient-initiated weight loss interventions. A prior study revealed that 82% of inpatients with obesity were receptive to weight loss interventions.21 Patients most receptive to interventions were those who recognized their own obesity and believed that weight loss would lead to improved health.22 It has to be recognized that despite the availability of effective antiobesity medications (AOM), its utilization in the community remains poor, with a usage rate of 0.8% in AOM-eligible adults.23 Inpatient initiation of AOM could potentially improve the use of these medications. However, discussion on weight loss interventions should be initiated only after receiving permission to discuss weight in the context of how it relates to the patient's overall health and/or current condition. Let's meet patients where they are. If patients are receptive to weight loss interventions, discussions on target weight loss and barriers can be addressed during hospitalization. At the time of care transition, early discharge planning allows time to link patients to outpatient weight loss services. Communication between inpatient providers and outpatient primary care physicians would help patients stay focused on weight loss plans. An inpatient multidisciplinary approach involving case managers, social workers, nutritionists, pharmacists, physical therapists, nurses, and physicians is crucial to ensure uninterrupted transition of care to outpatient weight loss management.22, 24 The existence of weight bias in healthcare delivery is undeniable, leading to patients with obesity hesitant to seek medical assistance due to fears of stigmatization. Hospitalists can play a pivotal role in combating weight bias within healthcare settings. The author declares no conflict of interest.

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)0,004
score de la tête « metaresearch » (Gemma)0,004
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesCharge utile insuffisante (le modèle a refusé de juger)
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,157
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

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

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 Opus0,126
Tête enseignante GPT0,488
Écart entre enseignants0,362 · 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; les deux têtes enseignantes s’accordent sur ce qui est montré ici.

Devis d'étudeSans objet
Domainenon disponible
GenreEmpirique

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

En bref

Citations0
Publié2023
Routes d'admission1
Résumé présentoui

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