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Record W6941246288 · doi:10.13016/m2krcw-u2kw

IMPROVING MEDICAID ENROLLMENT AND POPULATION HEALTH:THREE PAPERS FOR STATES.

2018· other· en· W6941246288 on OpenAlexaboutno aff

Bibliographic record

VenueMaryland Shared Open Access Repository (USMAI Consortium) · 2018
Typeother
Languageen
FieldAgricultural and Biological Sciences
TopicMycorrhizal Fungi and Plant Interactions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicaidStaffingUnemploymentHealth carePopulationHealth insuranceHealth policyPublic healthPublic health insurance

Abstract

fetched live from OpenAlex

This dissertation consists of three research studies designed to assist states with improving the health of their populations. Due to states’ greatly-increased responsibilities for promoting, providing, and regulating their residents’ health insurance; their continuing responsibilities for public health promotion, prevention, and surveillance; and their growing understanding of the relationship between individual health and the well-being of communities, many states now see themselves as stewards of their populations’ health. Yet, states struggle to ensure that eligible individuals receive insurance. Plus, they lack robust systems to monitor the health of their populations. In addition, they face ongoing fiscal and staffing challenges that will make it difficult to satisfy these responsibilities in the foreseeable future. The first two studies utilize quantitative and data visualization techniques to describe state-level Medicaid and Children’s Health Insurance Program (CHIP) enrollment patterns and dynamics between 2000 and 2011 for the purposes of identifying policies and procedures to expedite eligibility determinations, renewals, and transfers and thereby improve program participation. Specifically, study one utilizes state administrative data and dummy variables representing eligibility policies and procedures to estimate the relationship between unemployment and enrollment during a period of significant economic and policy change. It finds that the Medicaid participation rate increases with the unemployment rate and with large expansions of eligibility criteria, such as an expansion to childless adults like that authorized under the Affordable Care Act (ACA). Study two, the first to demonstrate Medicaid enrollment seasonality, draws from a robust set of state-level administrative data to analyze month-to-month changes by eligibility category. The four eligibility categories—children, parents, aged, and disabled— show distinct and consistent enrollment patterns. Insights into these patterns can inform outreach efforts, as well as the development of eligibility policies and management strategies for preventing backlogs. The third study, which received Fulbright program support, draws lessons and recommendations for states for monitoring population health from a case study of population health monitoring in the Canadian province of Saskatchewan, a federal substate that shares many socioeconomic characteristics with its American counterparts and which has provided universal health insurance for over fifty years.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.055
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0160.000

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.

Opus teacher head0.026
GPT teacher head0.294
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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