Healthcare Attitudes, Knowledge, and Decision Making
Notice bibliographique
Résumé
Rising healthcare costs in the United States represent a major economic burden to citizens (Selden, 2009). Several large-scale surveys gauging Americans' opinions and knowledge of healthcare and healthcare reform (Kohut, Keeter, Doherty, Dimock, Remes, Suls, et al., 2009; Jones, 2009a; Jones, 2009b; Penn, Schoen & Berland, 2009; Saad, 2009) arose in response to the recent congressional debates about healthcare reform. To a lesser extent, healthcare decision-making has been investigated (e.g., Furnham, Meader, & McClelland, 1999; Furnham, Simmons, & McClelland, 2000). Our study examined the associations between participants' knowledge and attitudes about healthcare and healthcare reform. In addition, we experimentally investigated how participants make healthcare decisions when medical resources are rationed. We collected data several months prior to the passage of the 2010 Patient Protection and Affordable Care Act, a set of laws that substantially changed the way healthcare is administered in the U.S. Our goal was to examine knowledge, attitudes, and decision making on the eve of major healthcare reform. The rising costs of healthcare in the United States have had an adverse impact on American life. Healthcare costs have grown from 11% of the Gross Domestic Product (GDP) in 1987 to 16% in 2005 (Sood, Ghosh, & Escarce, 2009). Rising healthcare costs have a negative impact on U.S. families. Selden (2009) examined family healthcare spending during a period of one year. He found that about half of all yearly medical expenses occurred in a single month, indicating that families are prone to falling into sudden debt over healthcare costs. One quarter of the population experienced a single month in which healthcare costs exceeded 20% of their incomes. Families who had trouble paying medical bills were likely to incur additional debt (e.g., paying for groceries with credit cards). In 2009, American opinion reflected sensitivity to rising costs. For example, Americans rated affordability as the nation's biggest healthcare problem (Saad, 2009). Given American dissatisfaction with the healthcare system in 2009, one might have predicted that Americans would have been in support of healthcare reform. However, Americans were divided over whether or how to accomplish healthcare reform. In a randomly selected sample of American adults contacted by telephone (Jones, 2009a), 40% said they would support the passing of a new healthcare bill, while 36% would oppose it (25% had no opinion). Seniors (65 or over) were largely opposed to healthcare reform while young adults (18-29) were more supportive. Jones (2009b) found that the top reason for opposing healthcare reform was fear of increased government involvement in healthcare. Americans who supported healthcare reform rated the number of uninsured as their primary reason for supporting reform. Citizens' knowledge of healthcare issues and proposed reforms may have some association with attitudes. For example, people with little knowledge of reform proposals may show negative attitudes toward such proposals, perhaps due to unfamiliarity. Penn, et al. (2009) found that only a third of participants (37%) were able to correctly identify the definition of the term when given three response options in a multiple-choice format. In fact, only 56% of participants knew that the public option was part of the 2009 healthcare reform debate (Kohut et. al., 2009). Slightly more people (61%) were aware that the U.S. spends more money per person on healthcare than most European nations. The limited research that existed on citizens' knowledge of the healthcare system in 2009 indicates that knowledge of the issues was modest. To date, we are unaware of any study that has examined the association between knowledge of and attitudes toward healthcare issues. The high cost of healthcare necessarily results in inequities in care based on income. …
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 enseignantsNi 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.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».