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Record W1986314829 · doi:10.1136/oemed-2013-101717.180

180 Occupational epidemiology: A bibliometric analysis by country and era

2013· article· en· W1986314829 on OpenAlexaboutno aff
K M Venables

Bibliographic record

VenueOccupational and Environmental Medicine · 2013
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsnot available
Fundersnot available
KeywordsEpidemiologyPublishingPopulationDemographyGeographyMedicineHistoryLibrary sciencePolitical scienceSociologyLawPathologyComputer science

Abstract

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Objectives Bibliographic databases allow the study of historical trends in research output Methods Countries active in occupational epidemiology were identified using the EPICOH membership list. Seven countries had more than 5 member scientists: USA, Canada, Sweden, UK, Italy, France, and Netherlands. Populations in 2000 were obtained from the UN website. Papers were sought in PubMed using “occupation*” and “epidemiolog*” in Title/Abstract. Country was obtained from the “affiliation” field. Results 7,433 papers were retrieved, the earliest from the UK in 1937 [1]. An initially steep increase in publishing has decelerated, numbers quadrupling from the 1970s to 1980s, doubling from 1980s to 1990s, but increasing by only 30% from 1990s to 2000s. The seven active countries together published 42% (3,095) of the total retrieved. No papers were retrieved from these countries before 1980, so results comparing them relate to 1980–2012. After correcting for population size, Sweden had the highest publication rate of 18.1 per million population, followed by Netherlands and Canada (7.5 and 6.7). USA, UK, France, and Italy were similar (5.2, 4.9, 4.9, and 4.6). In absolute numbers, the USA was the most prolific (1,449). Conclusions These findings must be interpreted with caution because any word search is dependent on the use of language, which varies between countries and language groups, and over time. Also, the affiliation field refers only to the first author. With these caveats, this historical analysis supports some anecdotal impressions about occupational epidemiology: Nordic countries, relative to their size, have made a major contribution; historically, papers have come from a small pool of countries; the large volume of papers from the USA is likely to be influential; and the trend of accelerating research output seen in the twentieth century may have stabilised.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Other designhigh
models splitAgreement compares identical category sets and study designs across arms.

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)0.021
metaresearch head score (Gemma)0.125
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.125
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.008
Bibliometrics0.1440.174
Science and technology studies0.0010.001
Scholarly communication0.0050.005
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.002

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 head0.072
GPT teacher head0.440
Teacher spread0.368 · 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

Labeled directly by 2 models reading the full record.

Bibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designObservational · Other design
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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