Caring for individuals with eating disorders–how to improve care while reducing unnecessary spending?
Notice bibliographique
Résumé
Eating disorders are mental health conditions defined by abnormal eating behaviours that negatively affect a person's physical and/or mental health.Eating disorders typically include anorexia nervosa, bulimia nervosa, binge eating disorder, and other 'specified feeding or eating disorders'that do not meet the strict diagnostic criteria of the aforementioned conditions.These disorders have a combined lifetime prevalence of about 7% in women and 3% in men [1] and are frequently associated with psychiatric comorbidities, such as mood and anxiety disorders, post-traumatic stress disorder, and substance use disorders [2], often requiring longterm treatment [3].Eating disorders can also cause short-and long-term medical complications [4], such as cardiovascular and renal problems, gastrointestinal disturbances, fluid and electrolyte abnormalities, menstrual and fertility problems (among females), osteoporosis and osteopenia, and dental and dermatological problems [5,6].Moreover, anorexia nervosa has the highest mortality rate of any psychiatric disorder [7].Previous studies have shown that the economic burden of eating disorders is substantial [8][9][10].Due to the high costs of care in this population, well-organised efforts directed toward early intervention and active management of these individuals' physical and mental health are warranted.Furthermore, given the surge in eating disorders-related emergency department visits and medical hospitalizations (i.e., acute care) among young women in Canada throughout the pandemic [11], it is important to understand whether there are ways to improve care among this particular population.Many jurisdictions have implemented strategies, such as high-risk care management, to reduce costs and improve the quality of care among patients with high health care needs.High-risk care management involves the provision of intensive, one-on-one services by a health worker, such as a nurse, to patients with complex needs, such as those with an eating disorder.The idea behind these types of strategies/interventions is that the implementation of high-quality outpatient care may help reduce unnecessary acute care for these patients.For example, research suggests that stepped care models, where primary care clinicians play a greater role in service delivery, may be an option to improve patient outcomes in a cost-effective manner [12].However, it is unclear whether any costs can be reduced, and if so which, especially among patients who require costly care. How to reduce unnecessary health care spending?One potential way to decrease health care spending, without sacrificing high-quality care, may be to target preventable (i.e., potentially unnecessary) acute care among patients with high
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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 | 0,005 | 0,034 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,005 | 0,005 |
| Science ouverte | 0,001 | 0,004 |
| Intégrité de la recherche | 0,004 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,021 | 0,003 |
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 ».