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Record W4388183598 · doi:10.7249/rr2759

The Road to Reintegration: Status and Continuing Support of the U.S. Air Force's Wounded, Ill, and Injured

2023· article· en· W4388183598 on OpenAlexaboutno aff
Carra S. Sims, Christine Vaughan, John A Hamm, Brent W. Anderson, Angela Clague

Bibliographic record

VenueRAND Corporation eBooks · 2023
Typearticle
Languageen
FieldPsychology
TopicPosttraumatic Stress Disorder Research
Canadian institutionsnot available
FundersAir Force Surgeon GeneralEunice Kennedy Shriver National Institute of Child Health and Human Development
KeywordsMental healthHealth carePsychologyUnemploymentQuarter (Canadian coin)MedicinePsychiatryPolitical scienceGeography

Abstract

fetched live from OpenAlex

The U.S. Air Force asked RAND Project AIR FORCE (PAF) to help assess the well-being of its wounded members and the quality of services provided to facilitate their recovery and reintegration. RAND PAF fielded a survey in the fall of 2016 to assess wounded airmen's functioning in the domains of physical health, mental health, interpersonal relationships, unemployment, and financial status, as well as their utilization and perceptions of Air Force nonmedical programs for wounded airmen. The authors of this study invited all 713 wounded airmen enrolled in the Air Force Wounded Warrior program to complete the survey, and 270 airmen (38 percent) completed it. One-third of airmen reported difficulty obtaining care for physical or mental health conditions, and one-quarter expressed dissatisfaction with coordination of care. Similar proportions of airmen reported barriers to care for physical and mental health conditions. Difficulty scheduling appointments was the most commonly endorsed barrier for both types of conditions. Small but notable proportions of airmen reported potential social support deficits, unemployment, and financial problems. For many of the Air Force's programs for wounded airmen, over 80 percent of program users reported overall program satisfaction. The authors recommend that the Air Force consider focusing on improving care coordination, increasing health care system capacity, continuing employment assistance, and improving marketing of programs with low uptake.

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 score0.846
Threshold uncertainty score0.252

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 head0.037
GPT teacher head0.331
Teacher spread0.294 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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