Healthcare Attitudes, Knowledge, and Decision Making
Bibliographic record
Abstract
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. …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".