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Enregistrement W2783683092 · doi:10.1002/14651858.cd012921.pub2

Random drug and alcohol testing for preventing injury in workers

2020· review· en· W2783683092 sur OpenAlexaff
Charl Els, Tanya Jackson, Mathew T Milen, Diane Kunyk, Graeme Wyatt, Daniel Sowah, Reidar Hagtvedt, Danika Deibert, Sebastian Straube

Notice bibliographique

RevueCochrane Database of Systematic Reviews · 2020
Typereview
Langueen
DomaineMedicine
ThématiqueSubstance Abuse Treatment and Outcomes
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMedicineMEDLINEOccupational injuryCochrane LibraryOccupational safety and healthSystematic reviewInjury preventionPoison controlFamily medicineClinical trialRandomized controlled trialMedical emergencySurgeryInternal medicine

Résumé

récupéré en direct d'OpenAlex

BACKGROUND: Drug- and alcohol-related impairment in the workplace has been linked to an increased risk of injury for workers. Randomly testing populations of workers for these substances has become a practice in many jurisdictions, with the intention of reducing the risk of workplace incidents and accidents. Despite the proliferation of random drug and alcohol testing (RDAT), there is currently a lack of consensus about whether it is effective at preventing workplace injury, or improving other non-injury accident outcomes in the work place. OBJECTIVES: To assess the effectiveness of workplace RDAT to prevent injuries and improve non-injury accident outcomes (unplanned events that result in damage or loss of property) in workers compared with no workplace RDAT. SEARCH METHODS: We conducted a systematic literature search to identify eligible published and unpublished studies. The date of the last search was 1 November 2020. We searched the Cochrane Central Register of Controlled Trials (CENTRAL), MEDLINE, Embase, two other databases, Google Scholar, and three trials registers. We also screened the reference lists of relevant publications known to us. SELECTION CRITERIA: Study designs that were eligible for inclusion in our review included randomised controlled trials (RCTs), cluster-randomised trials (CRTs), interrupted time-series (ITS) studies, and controlled before-after (CBA) studies. Studies needed to evaluate the effectiveness of RDAT in preventing workplace injury or improving other non-injury workplace outcomes. We also considered unpublished data from clinical trial registries. We included employees working in all safety-sensitive occupations, except for commercial drivers, who are the subject of another Cochrane Review. DATA COLLECTION AND ANALYSIS: Independently, two review authors used a data collection form to extract relevant characteristics from the included study. They then analysed a line graph included in the study of the prevalence rate of alcohol violations per year. Independently, the review authors completed a GRADE assessment, as a means of rating the quality of the evidence. MAIN RESULTS: Although our searching originally identified 4198 unique hits, only one study was eligible for inclusion in this review. This was an ITS study that measured the effect of random alcohol testing (RAT) on the test positivity rate of employees of major airlines in the USA from 1995 to 2002. The study included data from 511,745 random alcohol tests, and reported no information about testing for other substances. The rate of positive results was the only outcome of interest reported by the study. The average rate of positive results found by RAT increased from 0.07% to 0.11% when the minimum percentage of workers who underwent RAT annually was reduced from 25% to 10%. Our analyses found this change to be a statistically significant increase (estimated change in level, where the level reflects the average percentage points of positive tests = 0.040, 95% confidence interval 0.005 to 0.075; P = 0.031). Our GRADE assessment, for the observed effect of lower minimum testing percentages associating with a higher rate of positive test results, found the quality of the evidence to be 'very low' across the five GRADE domains. The one included study did not address the following outcomes of interest: fatal injuries; non-fatal injuries; non-injury accidents; absenteeism; and adverse effects associated with RDAT. AUTHORS' CONCLUSIONS: In the aviation industry in the USA, the only setting for which the eligible study reported data, there was a statistically significant increase in the rate of positive RAT results following a reduction in the percentage of workers tested, which we deem to be clinically relevant. This result suggests an inverse relationship between the proportion of positive test results and the rate of testing, which is consistent with a deterrent effect for testing. No data were reported on adverse effects related to RDAT. We could not draw definitive conclusions regarding the effectiveness of RDAT for employees in safety-sensitive occupations (not including commercial driving), or with safety-sensitive job functions. We identified only one eligible study that reflected one industry in one country, was of non-randomised design, and tested only for alcohol, not for drugs or other substances. Our GRADE assessment resulted in a 'very low' rating for the quality of the evidence on the only outcome reported. The paucity of eligible research was a major limitation in our review, and additional studies evaluating the effect of RDAT on safety outcomes are needed.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,014
score de la tête « metaresearch » (Gemma)0,062
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Revue systématique · Signal consensuel: Revue systématique
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,014
Score d'incertitude au seuil0,073

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0140,062
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0070,007
Bibliométrie0,0060,005
Études des sciences et des technologies0,0010,001
Communication savante0,0030,003
Science ouverte0,0020,001
Intégrité de la recherche0,0030,002
Charge utile insuffisante (le modèle a refusé de juger)0,0100,001

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,154
Tête enseignante GPT0,407
Écart entre enseignants0,253 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeRevue systématique
Domainenon disponible
GenreSynthèse

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations12
Publié2020
Routes d'admission1
Résumé présentoui

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