Evaluation of the Drug Evaluation and Classification Program: A Critical Review of the Evidence
Notice bibliographique
Résumé
OBJECTIVE: A critical review of the existing evaluation studies on the Drug Evaluation and Classification (DEC) program was conducted to determine the validity and accuracy of the technique for identifying drivers under the influence of drugs. METHODS: Studies were divided into two categories--laboratory studies and field (i.e., enforcement) studies. A classification process was devised using common criteria based on the toxicology findings (i.e., drug positive or drug negative) and the opinion of the police officer who assessed the driver (i.e., drug positive or drug negative). A series of standard measures (Sensitivity, Specificity, False Alarm Rate, Miss Rate, Corroboration, and Accuracy) were calculated for each to assess the effectiveness of the DEC program. RESULTS: Laboratory studies do not provide overwhelming support for the accuracy with which officers trained in the DEC program can detect and identify the particular class(es) of drug involved based on psychophysical assessment alone. The detection and identification of the relatively low levels of drugs administered were typically better than chance but many cases were missed. The fact that some drugs were detected with greater accuracy than others suggests that the effects of these substances were more prominently manifested in the symptomology assessed by the DEC procedure. Although field enforcement studies are not as scientifically rigorous as laboratory studies, DEC assessments in an enforcement context have the benefit of information obtained from the arresting officer and from interviews with the suspect. In addition, the drug doses consumed by users are typically much higher than those permitted in controlled laboratory studies. In general, officers trained in the DEC program are able to identify persons under the influence of drugs and to specify the drug class responsible with a degree of accuracy that not only exceeds chance, but in some cases reaches a very high level. CONCLUSIONS: There remains room for improvement in the DEC program. As further research becomes available, either from laboratory or field investigations or both, it needs to be incorporated into the program to enhance its validity and accuracy.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,027 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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.
score_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 découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».