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Record W584856053

Weltweite Trends bei Fahren unter Alkoholeinfluss - Erfahrungen der Vergangenheit und zukuenftige Entwicklungen

2008· article· de· W584856053 on OpenAlexaboutno aff
Kristine M. Stewart, B M Sweedler

Bibliographic record

VenueSCHRIFTENREIHE FAHREIGNUNG · 2008
Typearticle
Languagede
FieldMedicine
TopicSubstance Abuse Treatment and Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsPolitical scienceHumanitiesGynecologyMedicineArt
DOInot available

Abstract

fetched live from OpenAlex

Bei der Reduktion von Trunkenheit am Steuer wurden in den 1980er und 1990er Jahren in vielen westlichen Staaten bedeutende Fortschritte erzielt. Als Gruende fuer den Rueckgang gelten eine veraenderte oeffentliche Einstellung sowie wirksamere Gesetze und deren strengerer Vollzug. In den letzten Jahren sind die Entwicklungen unterschiedlich verlaufen. Waehrend in Frankreich und Deutschland die durch Alkohol verursachten Unfaelle weiter reduziert wurden, stiegen die entsprechenden Raten in anderen Laendern wieder an oder stagnierten. Der Beitrag stellt die Entwicklung von alkoholbedingten Strassenverkehrsunfaellen in folgenden Laendern dar: Australien, Kanada, Frankreich, Deutschland, Niederlande, Schweden, Vereinigtes Koenigreich und USA. Neben statistischen Daten werden die Massnahmen, die zur Reduktion der Alkoholunfaelle beitrugen, aufgelistet. Zusaetzlich wird ein Ausblick auf die zukuenftige Entwicklung gegeben. Strengere Gesetze und haeufigere polizeiliche Alkoholkontrollen haben zwar positive Auswirkungen, aber neue Anstrengungen zur Reduktion der Alkoholunfaelle sind erforderlich. So koennen Innovationen auf dem Gebiet der Fahrzeugtechnologie, wie etwa eine automatische Zuendsperre bei zu hohem Blutalkoholgehalt, die Verkehrssicherheit verbessern. Abschliessend wird auf die wichtige Rolle von Interessengruppen fuer die Umsetzung von Massnahmen fuer die Verkehrssicherheit hingewiesen. Zur Gesamtaufnahme siehe ITRD D361989. (KfV/K) ABSTRACT IN ENGLISH: In the past 20 years, considerable progress has been made in reducing impaired driving in most European countries, as well as in the United States, Canada, and Australia. These reductions appear to have resulted from changing public attitudes about drinking and driving, more effective laws, and vigorous enforcement that has deterred drinking and driving. Some countries have continued to make progress while in many countries this progress has stalled. New efforts seem to be needed to take us to the next step in safety. Stronger implementation of known effective strategies may help. Perhaps even more promising, technological advances may provide ways of both controlling the behavior of known drinking drivers and preventing impaired driving among the general population. Vehicle-based technologies include alcohol ignition interlocks that can be installed on the cars of known impaired drivers. These devices are already available and their wider use could have a significant impact on safety. Technologies in development can use a variety of devices to assess whether a driver is impaired by alcohol or other problems and can prevent driving while unfit. These devices would be passive and unobtrusive to the unimpaired driver, coming into play only when illegal alcohol levels or significant other impairment are detected. While many strategies for reducing the toll of impaired driving are known and in development, the most important factor is often the political will to make needed changes and investments. The paper will also address ways in which researchers, citizen advocates, and policy makers can motivate and facilitate change. (A)

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)0.002
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 score0.073
Threshold uncertainty score0.146

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.031
GPT teacher head0.289
Teacher spread0.258 · 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

Citations0
Published2008
Admission routes1
Has abstractyes

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