UNDERSTANDING THE COMPLEXITY AND USE CHALLENGES OF MEDICARE PLAN FINDER FOR OLDER ADULTS
Bibliographic record
Abstract
Abstract It is critical for older adults to find an affordable health plan that meets their needs. The Medicare.gov site provides a Medicare plan finder (MPF), which is an online tool designed to assist individuals to find a Medicare plan that is most suitable for them. The MPF was updated in 2021 but remains complex and may be especially difficult for older adults to use, given age-related changes in cognitive abilities. To identify potential use challenges for MFP, we documented the complexity of the steps required for accomplishing the goal of finding a plan. We completed a task analysis using two use-case scenarios to illustrate potential paths one could take in using MPF. Both use cases were set to enroll in the same plan to explore how and why their paths could differ, focusing on whether they have prior information and experience with Medicare and MPF. The number of steps taken by these two use cases ranged from 10 steps (experienced user) to 32 steps (novice user), due to the different needs they had for using MPF. The results imply that the one-for-all design of MPF may not be optimal for meeting the different needs of users. We propose two solutions to reduce the number of steps for using MPF. First, users need to be able to access the functions they want from the start (e.g., using quick links from the initial page). Second, users would benefit from learning what they need to know and prepare before they start using MPF.
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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 | 0.015 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".