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Record W2175938034 · doi:10.7205/milmed-d-14-00250

Health Care Utilization Behavior of Veterans Who Deployed to Afghanistan and Iraq

2015· article· en· W2175938034 on OpenAlexaff
Seung Eun Lee, Vincent P. Fonseca, Charles Wolters, Deborah D. Dougherty, Michael R. Peterson, Aaron Schneiderman, Erick K. Ishii

Bibliographic record

VenueMilitary Medicine · 2015
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsLockheed Martin (Canada)
FundersU.S. Department of Defense
KeywordsIraq warMilitary medicineMedicineEnvironmental healthPublic healthNavyMilitary personnelGerontologyHealth careNursingPolitical science

Abstract

fetched live from OpenAlex

BACKGROUND: Previous assessments of Afghanistan/Iraq Veterans have lacked a systematic overview of all injury and illness experiences captured by the Veterans Health Administration (VHA) health care services. In this initial study, we quantify the health care utilization behavior of eligible Veterans and describe the level and type of usage among them. METHODS: A roster of service members who have served in Afghanistan/Iraq and became eligible for VHA care between 2002 and 2010 and their corresponding administrative VA medical encounter data were abstracted from the VHA Office of Public Health Operation Enduring Freedom/Operation Iraqi Freedom/Operation New Dawn Health Surveillance System. RESULTS: Between 2002 and 2010, approximately 55% of eligible Veterans accessed VHA health care. Higher utilization was observed among Veterans 50 years of age and older compared to younger Veterans. Higher utilization was also observed among Veterans with increasing cumulative deployment time. Mental disorder diagnostic codes accounted for the greatest number of visits per Veteran. CONCLUSIONS: Veterans with mental health diagnoses may need a different level of care than other VHA users. Other service factors associated with utilization require further research to better understand the underlying relationship. Current observed results may be reflective of future expected utilization patterns and may assist in resource planning and research.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.709
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

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

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 head0.196
GPT teacher head0.464
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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations11
Published2015
Admission routes1
Has abstractyes

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