Breaking Health Insurance Knowledge Barriers Through Games: Pilot Test of Health Care America
Bibliographic record
Abstract
Background: Having health insurance is associated with a number of beneficial health outcomes. However, previous research suggests that patients tend to avoid health insurance information and often misunderstand or lack knowledge about many health insurance terms. Health insurance knowledge is particularly low among young adults. Objective: The purpose of this study was to design and test an interactive newsgame (newsgames are games that apply journalistic principles in their creation, for example, gathering stories to immerse the player in narratives) about health insurance. This game included entry-level information through scenarios and was designed through the collation of national news stories, local personal accounts, and health insurance company information. Methods: A total of 72 (N=72) participants completed in-person, individual gaming sessions. Participants completed a survey before and after game play. Results: Participants indicated a greater self-reported understanding of how to use health insurance from pre- (mean=3.38, SD=0.98) to postgame play (mean=3.76, SD=0.76); t71=−3.56, P=.001. For all health insurance terms, participants self-reported a greater understanding following game play. Finally, participants provided a greater number of correct definitions for terms after playing the game, (mean=3.91, SD=2.15) than they did before game play (mean=2.59, SD=1.68); t31=−3.61, P=.001. Significant differences from pre- to postgame play differed by health insurance term. Conclusions: A game is a practical solution to a difficult health issue—the game can be played anywhere, including on a mobile device, is interactive and will thus engage an apathetic audience, and is cost-efficient in its execution.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".