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Record W2434905100

[Tobacco smoking among Italian physicians].

2008· article· en· W2434905100 on OpenAlexaboutno aff
Derek Smith, N L'Abbate, Anna Maria Lorusso

Bibliographic record

VenuePubMed · 2008
Typearticle
Languageen
FieldPsychology
TopicStress and Burnout Research
Canadian institutionsnot available
Fundersnot available
KeywordsSmoking prevalenceMedicineQuarter (Canadian coin)Environmental healthPaceDemographyHealth carePopulationGeographyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

A survey of the international literature published between 1985 and 2000 reveals high smoking rates among Italian physicians. Since 1985 smoking rates among physicians have gradually declined, similarly to those of general population. However, prevalence rates seem to vary between northern and southern Italy, with the highest rates in the southern regions. Studies examining smoking rates by gender reveal that, while smoking trends among males have declined somewhat, the latter remained relatively stable among female physicians. From an international perspective, declines in the absolute smoking rates of Italian physicians have not kept pace with those of many other countries, since at least one-quarter of Italian physicians currently smoke. Furthermore, most of the Italian physicians smoke while they are at work. This represents an occupational health problem that needs to be addressed by all levels of management. In meeting this challenge, occupational medicine has an important role to play in helping reduce the prevalence of smoking among health care workers.

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 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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.026
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.067
GPT teacher head0.299
Teacher spread0.232 · 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 source (direct Gemma or distilled Codex), 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
Published2008
Admission routes1
Has abstractyes

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