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Record W2129018756 · doi:10.1093/occmed/kqt076

Mortality in NHS Greater Glasgow and Clyde employees: 2007-2009

2013· article· en· W2129018756 on OpenAlexaff
K. Freer, Eugene Waclawski

Bibliographic record

VenueOccupational Medicine · 2013
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineDeath certificateWorkforceMortality rateOccupational safety and healthDemographyCause of deathGerontologyFamily medicineDiseaseSurgeryInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Just over a fifth of all deaths in Scotland occur in those under the age of 65. This study examined deaths in service in employees of the National Health Service Greater Glasgow and Clyde (NHS GG&C) Health Board over a 3-year period. AIMS: To assess crude death rates by occupational group, the main causes of death and evidence of causes that could have been prevented or modified by lifestyle changes. METHODS: Demographic details, occupational grouping and death certificate data were obtained for all NHS GG&C employees who died in service between 2007 and 2009. RESULTS: A total of 138 employees died in this period. The occupational groups in which most deaths occurred were support services (porters, domestic and catering staff; 35%) and nurses (34%). The commonest causes of death were lung cancer (15%), ischaemic heart disease (9%) and suicide (9%). The overall crude death rate was 1.2/1000 persons/year (females 1.0 and males 1.7) and was highest among support services employees (2.4) and lowest among medical staff (0.5). The relative risk of death in support services was significantly greater than the majority of occupational groups. CONCLUSIONS: These findings suggest health inequality within this workforce. The main causes of death identified in the support services group could potentially be modified through workplace risk factor screening and health promotion.

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 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 score0.047
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0060.001

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 head0.055
GPT teacher head0.418
Teacher spread0.363 · 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
Published2013
Admission routes1
Has abstractyes

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