MétaCan
Menu
Back to cohort
Record W2915545775 · doi:10.5539/gjhs.v11n3p63

Effects of Occupational Noise on Blood Pressure

2019· article· en· W2915545775 on OpenAlexvenueno aff
Bright Otoghile, Johnson Ediale, Nasir Olakunle Ariyibi, Okubokekeme Otoru Otoru, Joseph Iraskeb Kuni, Nuhu D Ma’an

Bibliographic record

VenueGlobal Journal of Health Science · 2019
Typearticle
Languageen
FieldHealth Professions
TopicNoise Effects and Management
Canadian institutionsnot available
Fundersnot available
KeywordsBlood pressureNoise (video)MedicineDiastoleNoise levelCardiologySound pressureInternal medicineAudiologyAnesthesiaAcousticsPhysicsHearing lossComputer science

Abstract

fetched live from OpenAlex

BACKGROUND: Noise has been found to have non auditory effects. One of the possible non auditory effects of noise is its effect on blood pressure. Available data on the effect of noise on blood pressure has been found to vary. Hence, the aim of this study was to find if there is a predictive effect of noise on blood pressure. METHOD: Study was done among sawmill workers in Ile-Ife. The noise in each sawmill was measured with a sound meter and blood pressure of each participants were recorded. A regression analysis was done using systolic and diastolic blood pressures as dependent variables and noise as the predictor. RESULTS: A total of 420 sawmill workers were recruited into the study with an average age of 33.53±8.59 years. The average noise level in the sawmill was 88±1.87 dB and the average systolic and diastolic blood pressures were 132 ± 21mmHg and 85 ± 13mmHg respectively. There was no significant effect of noise on systolic blood pressure {F (1, 419) = 0.958, P>0.05} but there was a significant effect of noise on diastolic pressure {F (1, 419) = 7.543, P<0.05}. CONCLUSION: This study found that exposure to noise is a predisposing factor to increase in blood pressure.

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)0.003
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 score0.170
Threshold uncertainty score0.321

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
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 head0.024
GPT teacher head0.421
Teacher spread0.396 · 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

Citations2
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueGlobal Journal of Health ScienceSame topicNoise Effects and ManagementFrench-language works237,207