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Record W3201895397 · doi:10.1016/j.ergon.2021.103227

Protocols used to determine the influence of backpack load on physiological variables. Systematic review

2021· article· en· W3201895397 on OpenAlexaff
Aitor Pinedo‐Jauregi, Gaizka Mejuto Hidalgo, David J. Bentley, Paul Grimshaw, Jesús Cámara

Bibliographic record

VenueInternational Journal of Industrial Ergonomics · 2021
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Performance
Canadian institutionsOntario Tech University
FundersEusko Jaurlaritza
KeywordsBackpackMedicineProtocol (science)Physical medicine and rehabilitationPsychological interventionPhysical therapyEngineeringAlternative medicinePathology

Abstract

fetched live from OpenAlex

Professional mountain rescue mountain groups use backpack equipment in their professional activities. The velocity of ambulation, gradient, load and the participant's physical characteristics have been described in the scientific literature as influential factors on response to exercise. The purpose of the present systematic review is to assess the protocols used to investigate the effects of backpacks and their influence on physiological responses at laboratory. A total of 14 articles were included in the review. Most research studies indicated participants were not experienced with backpack carriage. We observed a certain threshold on physiological changes in response to exercise was between 20 and 40 kg of backpack load. In conclusion, there is a heterogeneity of protocols used at the laboratory, hampering the comparison between different results. Future research should focus on the design of protocols that reproduce real scenarios of targeted populations. Rescue groups, firefighters and military personnel carry load with backpack in emergency interventions. This review analyzes different types of methodological protocols that investigate the influence of backpack load on physiological responses during exercise. The result will help manufacturer design backpacks considering the physiological burden of backpack carriage.

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)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.562
Threshold uncertainty score0.718

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.224
GPT teacher head0.469
Teacher spread0.246 · 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 designSystematic review
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
Published2021
Admission routes1
Has abstractyes

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