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Record W4406130016 · doi:10.4236/odem.2025.131003

Resting Blood Pressure of Workers in High Stress Urban Occupations

2025· article· en· W4406130016 on OpenAlexafffund
Stephanie P. B. Caligiuri, Jo-Anne Gilchrist, Teal B. Pierce, Jose Alejandro Austria, Susan D. Hirst, Broderick C. Hirst, Beverley Gail Pierce, Grant N. Pierce

Bibliographic record

VenueOccupational Diseases and Environmental Medicine · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicClimate Change and Health Impacts
Canadian institutionsUniversity of ManitobaSt. Boniface Hospital
FundersCanadian Institutes of Health Research
KeywordsBlood pressureMedicineInternal medicine

Abstract

fetched live from OpenAlex

Background: First responders (firefighters, paramedics, and police) working in an urban setting can be exposed to a high-stress environment caused by strenuous physical exertion, potentially dangerous work conditions, sleep deprivation due to shift work, poor dietary habits, psychological stress and noise levels that are excessive. This may induce chronic increases in blood pressure. The purpose of this study was to determine the presence of hypertension in people working in occupations generally accepted as high stress in comparison to those working in an environment where less of these obvious high stressors were present. Methods: Resting blood pressure was measured by TrUBP in 1067 on-duty first responders (fire, paramedic, and police), and in participants generally associated with a lower-stress work environment (transit workers, city and bank employees, factory workers and legislature employees). Results: The average age, systolic and diastolic blood pressures were significantly lower in those employees working in a high-stress environment than those in a low-stress job. This difference was observed in both male and female sexes. Conclusions: Our data do not support an association of high resting blood pressure values in those employed in activities typically associated with a high-stress urban working environment.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.018
GPT teacher head0.289
Teacher spread0.271 · 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 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

Citations0
Published2025
Admission routes2
Has abstractyes

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