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Enregistrement W1587918416 · doi:10.1002/j.2051-5545.2010.tb00281.x

Problematic Internet use and the diagnostic journey

2010· article· en· W1587918416 sur OpenAlexaff
Nady el‐Guebaly, Tanya Mudry

Notice bibliographique

RevueWorld Psychiatry · 2010
Typearticle
Langueen
DomaineSocial Sciences
ThématiqueImpact of Technology on Adolescents
Établissements canadiensFoothills Medical CentreUniversity of Calgary
Organismes subventionnairesnon disponible
Mots-clésAddictionThe InternetTerminologyTypologyBehavioral addictionCravingPsychologyPornographySexual addictionSalience (neuroscience)DSM-5Sexual abusePsychiatryClinical psychologyMedicineCognitive psychologyPoison controlInjury prevention

Résumé

récupéré en direct d'OpenAlex

Elias Aboujaoude's thoughtful overview of problematic Internet use allows us to reflect on the various strategies currently available to buttress claims for a distinct diagnostic category along the impulsive, compulsive, and/or addiction disorders spectrum. The presence of physiological tolerance and withdrawal, which initially underpinned the diagnosis of substance dependence, has become increasingly optional in recent nomenclatures, while behaviors such as impaired control, preoccupation/compulsive use, continued use despite harmful consequences, and craving have been added. These behavioral constructs have ushered the consideration of an ever-growing list of activities liable to excess, without the physiological requirements. These adapted criteria borrow terminology from related disorders but, so far, have received only limited empirical testing. The DSM nomenclature also utilizes “exclusion criteria” such as “the behavior is not better accounted for by a manic episode”, disclaiming the real possibility of two primary disorders. Further, the categorical dichotomy abuse/dependence is increasingly recognized as lacking empirical support, many arguing for a continuum perspective for excessive behaviors. Among the attempts to identify core components of behavioral addiction, including Internet addiction, Griffiths 1 has suggested salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse. Other researchers have created a typology of Internet addiction such as online gaming, online sexual preoccupations, and emailing/texting 2. Could the Internet just be the medium used to enact or fuel excessive behaviors? 1. Those displaying problematic Internet use through activities such as online gambling, shopping, sexual activity or viewing pornography, may be selecting to conduct their chosen behavior on the Internet. If the Internet were not available, these same persons would spend their time at casinos, shopping malls, utilizing the sex trade industry, or viewing print pornography. Finally, an additional measure to consider is the Problematic Internet Use Questionnaire (PIUQ), created by Thatcher and Goolam 3. Based on the South Oaks Gambling Screen (SOGS) and Young's Internet Addiction Scale (IAS), this measure is self-completed and contains 20 items on a 5 point Likert scale, from 1 (rarely/not applicable) to 5 (always). The measure taps into three factors: online preoccupations (10 items), adverse effects (7 items), and social interactions (3 items). The results of a pilot and larger validation studies provided good evidence for the reliability and construct validity of the PIUQ; however, participants were recruited via an online IT magazine, which may reflect a sampling bias. Prevalence estimates among interest groups, such as Internet users or treatment samples, are fraught with sampling bias. Studies examining behavioral addictions may overestimate the prevalence of these phenomena, as they often sample from populations already engaged in these excessive behaviors. Of note, in contrast with the common occurrence of the behaviors investigated, i.e., exercise, sex, or Internet use, the point prevalence of these problematic behaviors in the general population is typically low, i.e., less than 1% for the severe end of the spectrum, as in the case of Internet addiction or pathological gambling, with an average 2–3% added for less severe problematic use. Weakening the epidemiological perspective is the lack of estimates of incidence, due to a dearth of longitudinal prospective studies. In candidate disorders derived from a rapidly developing technology such as the Internet, longitudinal studies exploring rates of incidence should be a priority to shed further light on groups at particular risk. Associated “birds of a feather” provide insights about potential etiological links. High comorbidity rates with substance abuse, for example, have buttressed the consideration of behavioral disorders under the “addiction” umbrella, such as is the case in pathological gambling. In our experience, features of impulsivity, compulsivity, and addiction are encountered in most of our clinical samples and, to various degrees, in individual patients. The relative frequency of these features remains an open question. It is sometimes assumed that, if a treatment strategy applied to a specific disorder is equally successful with another disorder, this may argue in favor of classifying both disorders together. A string of pharmacological trials applied to severe problematic behaviors based on a putative affiliation with impulsive, compulsive, or addiction disorders have, so far, resulted in limited benefit. The quest continues. By comparison, psychotherapeutic approaches, particularly cognitive behavioral therapy, are currently supported by the broadest empirical evidence over longer follow-up periods. Should we further explore a nomenclature of cognitive distortions rather than investigating new disease entities? Lastly, the popularity of 12-steps mutual help groups have been seen by some as further evidence of the benefit of an addiction model applied to problematic behaviors. Renewed impetus in the expanded consideration of a range of excessive behaviors as disorders has arisen from tremendous advances in the study of the brain. Brain imaging yields images of a common pathway through the “reward system” without the confound of the use of a substance. “Reward circuit” disorders may be a gateway to the exploration of human nature itself, rather than just “impulses”, “compulsions”, or “addictions”. Aboujaoude's review also raises, for us, the unaddressed question of socio-cultural relevance. Certain cultural communities are emerging as being more at risk than others. In South Korea, children diagnosed with Internet addiction may even require hospitalization. Are the children more at risk for Internet addiction in South Korea, or is the behavior simply less socially acceptable in that culture? The behaviors explored, i.e., work exercise, sex, gambling, and Internet use, are culturally value laden. Their liability to become excessive could also be shaped by culture. The investigative journey continues toward constructs with improved predictive validity and effective management strategies.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,112
Score d'incertitude au seuil0,985

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,001
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,001
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

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.

Tête enseignante Opus0,015
Tête enseignante GPT0,292
Écart entre enseignants0,277 · 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 tête enseignante, pas un consensus.

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

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

En bref

Citations3
Publié2010
Routes d'admission1
Résumé présentoui

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