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Record W1918781180 · doi:10.7205/milmed.172.1.63

Attrition of U.S. Military Enlistees with Waivers for Hearing Deficiency, 1995–2004

2007· article· en· W1918781180 on OpenAlexaff
David W. Niebuhr, Yuanzhang Li, Timothy E. Powers, Margot R. Krauss, David W. Chandler, Thomas M. Helfer

Bibliographic record

VenueMilitary Medicine · 2007
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsKensington Health
FundersU.S. Department of Defense
KeywordsAttritionMilitary personnelMilitary medicineMedicineNavyAudiologyGerontologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

BACKGROUND: Hearing deficiency is the condition for which accession medical waivers are most commonly granted. The retention of individuals entering service with a waiver for hearing deficiency has not been previously studied. METHODS: Military retention among new enlistees with a medical waiver for hearing deficiency was compared with that among a matched comparison group of fully qualified enlistees. Comparisons according to branch of service over the first 3 years of service were performed with the Kaplan-Meier product-limit method and proportional-hazards model. RESULTS: Army subjects had significantly lower retention rates than did their fully qualified counterparts. In the adjusted model, Army and Navy enlistees with a waiver for hearing deficiency had a significantly lower likelihood of retention than did their matched counterparts. DISCUSSION: The increased likelihood of medical attrition in enlistees with a waiver for hearing loss provides no evidence to make the hearing accession standard more lenient and validates a selective hearing loss waiver policy.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.044
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.095
GPT teacher head0.430
Teacher spread0.335 · 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

Citations8
Published2007
Admission routes1
Has abstractyes

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