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Record W4402020929 · doi:10.1136/military-2024-002735

Clothing and individual equipment for the female soldier: developing a framework to improve the evidence base which informs future design and evaluation

2024· review· en· W4402020929 on OpenAlexaff
N. Armstrong, SA Rodrigues, KM Gruevski, KB Mitchell, Alison L. Fogarty, Stephen Graham Saunders, Laura Bossi

Bibliographic record

VenueBMJ Military Health · 2024
Typereview
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsDefence Research and Development Canada
FundersDefence Science and Technology Laboratory
KeywordsClothingWork (physics)Engineering managementProcess managementEngineeringEngineering ethicsPolitical science

Abstract

fetched live from OpenAlex

The development of inclusive equipment and clothing is a priority across national defence departments that are part of The Technical Cooperation Programme. As such, a collaborative effort has been established to inform the development of clothing and equipment for women. This invited review provides an overview of an ongoing collaborative project presented at the sixth International Congress on Soldiers Physical Performance. The purpose of this review was to summarise the outputs of scoping work conducted to inform the direction of future research programmes. The scoping work has recommended a framework, which includes improved objective metrics for assessment, standardised methods to characterise study participants and improved methods for characterising the system being evaluated. The longer-term research project aims to implement the framework so that the design of future equipment and clothing is optimised for all end users.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.101
metaresearch head score (Gemma)0.149
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.101
Threshold uncertainty score0.537

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1010.149
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0090.007
Bibliometrics0.0200.011
Science and technology studies0.0020.004
Scholarly communication0.0120.011
Open science0.0050.007
Research integrity0.0070.006
Insufficient payload (model declined to judge)0.0050.001

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 head0.374
GPT teacher head0.566
Teacher spread0.192 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations4
Published2024
Admission routes1
Has abstractyes

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