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Enregistrement W2083886771 · doi:10.1177/1715163514521965

The Oz craze

2014· article· fr· W2083886771 sur OpenAlexaffvenueabout
Nicole Bootsman, David Blackburn, Jeff Taylor

Notice bibliographique

RevueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2014
Typearticle
Languefr
DomaineArts and Humanities
ThématiqueMedia Influence and Health
Établissements canadiensUniversity of Saskatchewan
Organismes subventionnairesnon disponible
Mots-clésPsychologyMaterials science

Résumé

récupéré en direct d'OpenAlex

Health care professionals often cringe when they hear the name “Dr. Oz.” This physician turned television personality has become a household name and a trusted source of health care information for many people across North America. Indeed, 3.4 million viewers1 tune into his show every day to hear about health conditions, treatments, tests and many other topics relevant to health care. While Dr. Oz has received considerable interest in recent years, pop culture media’s influence on health behaviour is not new. Oprah Winfrey has given us advice on dieting, Tom Cruise has warned us against antidepressant use in postpartum depression, while Jenny McCarthy advises against vaccinating our kids because of the risk for autism. Alternative sources of health information are so prevalent that it would be naive for health care professionals to assume that patients are completely loyal to the information that they provide. Currently, there is little objective information to quantify the effect of pop culture media on health behaviour. However, the signals are clear: after being featured on “The Dr. Oz Show,”2 neti pot sales increased by 12,000%, with Internet searches on the topic increasing 42,000%. In addition, a simple analysis using Google Trends shows a higher use of search terms for raspberry ketones3 and green coffee bean extract4 following episodes of “The Dr. Oz Show”5,6 in which these products were discussed (Figure 1). In each case, Internet searches with these terms were virtually nonexistent until the dates on which these episodes were aired. It is plausible that pharmacists and drug information centres also received requests for information during these same times. Figure 1 The episode air and re-air dates (as indicated by the arrows) for raspberry ketones5 and green coffee bean extract6 Few would argue that media sources often increase awareness about alternative health products, while simultaneously raising concerns about current health care practices. However, health care practitioners have no way of quantifying the prevalence or consequences of these trends. In our view, the frequent use of search terms identified with Google Trends could be a marker of the level of interest in a particular health product. Further, high levels of interest may also be associated with higher product sales. Personal communication with a single natural product store in Lloydminster, Alberta, supports this hypothesis. Raspberry ketones were introduced to the store product line 5 weeks prior to the corresponding Dr. Oz episode, and sales appeared to increase sharply thereafter (Figure 2). Figure 2 Sales data on raspberry ketones from a natural product store The influence of media is not just restricted to sales of alternative products. An episode from “The Dr. Oz Show” focusing on the necessity of an “LDL particle size test”7 was followed by a spike in searches on Google Trends (Figure 3). This observation raises a few questions. First, does the increased level of interest in a blood test translate into higher usage in clinical settings? Second, if health services such as blood tests are vulnerable to pop media influences, who is paying the costs of these tests? Patients may be paying out of pocket to have these tests performed, but it is also possible that health insurance organizations or governments are ending up with the bill. Currently, we can find no information on the extent to which health insurance providers or governments are affected by social media. Figure 3 Internet search interest for the “LDL particle size test.” The peak points directly correspond to the episode air dates8 Patient nonadherence could be another important consequence of media influences. Take for example a recent episode of “The Dr. Oz Show” entitled, “The Doctors Who Say Everything You Know About Cholesterol Is Wrong.”7 Not surprisingly, issues around statin use for high cholesterol were discussed, and viewers (especially those taking statins) could have easily concluded that their prescriptions were inappropriate. Ultimately, negative health outcomes could occur if media sources are influencing patients to take unproven products, quit effective medications or undergo unsanctioned procedures. There is already evidence for the dire consequences of medication nonadherence, but our understanding of its causes remains limited. Perhaps a greater understanding of how the media influence health behaviour might help us to devise more effective strategies to reduce these problems. Moreover, health care practitioners urgently require training on how to help patients navigate and interpret the ever-growing number of messages from media sources. At minimum, we need to be able to effectively reduce bias in our interactions with patients.9 For now, it must be recognized that sources of media are conveying health-related messages that are likely influencing patients.10 Health care professionals must not dismiss information obtained by patients regardless of the credibility of the source. Instead, it is crucial for health care professionals to recognize that their patients simply want to get better. Open dialogue with an empathetic response to their desire for good news about safe remedies is the best approach to establishing trust and facilitating safe medication use. Remember that patients probably cringe every time they hear health care practitioners criticize alternative therapies. ■

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)0,001
score de la tête « metaresearch » (Gemma)0,004
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesCharge utile insuffisante (le modèle a refusé de juger)
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Commentaire · Signal consensuel: aucune
Score de désaccord entre enseignants0,379
Score d'incertitude au seuil0,886

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,004
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,002
Communication savante0,0060,004
Science ouverte0,0010,006
Intégrité de la recherche0,0020,003
Charge utile insuffisante (le modèle a refusé de juger)0,3790,167

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 Opus0,037
Tête enseignante GPT0,256
Écart entre enseignants0,219 · 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.

Devis d'étudeSans objet
Domainenon disponible
GenreCommentaire

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

En bref

Citations11
Publié2014
Routes d'admission3
Résumé présentoui

Explorer davantage

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