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Record W4396616917 · doi:10.1007/s11920-024-01497-1

Insufficient Sleep and Behavioral Health in the Military: A 5-Country Perspective

2024· review· en· W4396616917 on OpenAlexaffabout
Sara E. Alger, Clare Bennett, Neanne Bennett, Matthew G. Huebner, Jennifer E. C. Lee, Heather J. McCuaig Edge, Amos Simms, Amy B. Adler

Bibliographic record

VenueCurrent Psychiatry Reports · 2024
Typereview
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsDepartment of National Defence
FundersNaval Medical Center San Diego
KeywordsSleep (system call)InsomniaPerspective (graphical)Health promotionMedicinePromotion (chess)PsychologyGerontologyPsychiatryClinical psychologyPublic healthPolitical scienceNursing

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The goal of this paper was to highlight the degree to which sleep, behavioral health, and leader involvement were interrelated using data from militaries in five English-speaking countries: Australia, Canada, New Zealand, the UK, and the United States. RECENT FINDINGS: Many service members reported sleeping fewer than the recommended 7 h/night: 34.9%, 67.2%, and 77.2% of respondents from New Zealand, Canada, and the United States, respectively. Countries reporting shorter sleep duration also reported fewer insomnia-related difficulties, likely reflecting higher sleep pressure from chronic sleep loss. Across all countries, sleep problems were positively correlated with behavioral health symptoms. Importantly, leader promotion of healthy sleep was positively correlated with more sleep and negatively correlated with sleep problems and behavioral health symptoms. Insufficient sleep in the military is ubiquitous, with serious implications for the behavioral health and functioning of service members. Leaders should attend to these risks and examine ways to promote healthy sleep in service members.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.898
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.043
GPT teacher head0.420
Teacher spread0.377 · 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
GenreReview

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

Citations7
Published2024
Admission routes2
Has abstractyes

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