Notice bibliographique
Résumé
Dear Editor, I read the AFPI position paper on road safety and public health with interest.[1] The authors mentioned the use of alcohol, co-morbid medical conditions (diabetes mellitus, Parkinson's disease, Alzheimer's disease, epilepsy), and adverse drug reactions among the risk factors for road traffic accidents. Psychiatric illnesses are also an important risk factor for road traffic accidents. It is well known that many psychiatric disorders can lead to impairment in the level of cognitive and executive functioning required for safe driving, and medications used to treat them can also potentially cause disruption in perception, information processing, and overall psychomotor activity.[23] Moreover, studies have suggested that drivers with mental health conditions have a higher risk of being involved in a crash.[4] A recent systematic review tried to identify what is known about driving for people with mental health conditions, and critically appraise studies that empirically investigated assessment of fitness-to-drive among people with mental health conditions revealed many interesting findings.[5] Among patients with schizophrenia, even when stabilized with antipsychotic medication, great proportion of the patients were reported not fit-to-drive.[5] Among patients with major depressive disorder higher levels of sleepiness were found when driving, irrespective of medication use.[5] Moreover, depressive patients were also found to have slower steering reaction times and a greater number of car crashes when compared with controls.[5] Statistically higher crash rates were also identified in personality disorder group and in the psychoneurotic group when compared with controls.[5] However, the authors concluded that the overall quality of studies examining fitness-to-drive is low and large-scale longitudinal studies with age-matched controls are urgently needed in order to determine the effects of different conditions on fitness-to-drive.[5] Considering the above findings, it is important to assess each patient with psychiatric disorders to determine if the patient is fit-to-drive to reduce the risk of road traffic accidents. A study from the United Kingdom exploring whether the mental health practitioners were assessing their patients’ fitness-to-drive and addressing the issue as guided by the relevant agencies and legislation found that there was a poor compliance with the standards among assessing clinicians.[6] Another study exploring the practices of Canadian psychiatrists regarding fitness-to-drive in individuals with mental illness found that only 18.0% of respondents were always aware of whether their patients were active drivers.[7] The above study results indicate that there is a clear need for education and guidelines to assist psychiatrists in decision making about driving fitness. Though, there is no single assessment that can be used to accurately predict driving ability of people with psychiatric illnesses, it is recommended that a series of assessment methods including medical and occupational therapy assessments, neuropsychological tests, on-road assessment, and car driving simulator tests should be used to reach a conclusion regarding fitness-to-drive.[5] The Driver and Vehicle Licensing Agency (DVLA) in the United Kingdom also provides clear and detailed recommendations on minimum stand-down periods from driving relating to various psychiatric conditions.[8] Financial support and sponsorship Nil. Conflicts of interest There are no conflicts of interest.
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 | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 ».