Preferences for Accessing Medical Information in the Digital Age: Health Care Professional Survey.
Notice bibliographique
Résumé
BACKGROUND: Health care professionals (HCPs) routinely have questions concerning the medications they are recommending. There are numerous resources available; however, each has its own advantages and disadvantages. OBJECTIVE: The purpose of this survey was to gain knowledge of the preferred methods and sources HCPs use to obtain information concerning medications. METHODS: A total of 511 HCPs (202 physicians, 105 pharmacists, 100 advance practice nurses, 53 registered nurses, and 51 physician assistants) were surveyed through a third-party market research firm. All participants were practicing in the United States. Individuals working for a pharmaceutical company were excluded. The survey collected demographics, frequency of searching medical information, types of questions searched, sources of medical information, and rationale for preferred and nonpreferred sources of medical information. Use of medical information resources were rated on a 5-point ordinal scale. Data were analyzed with descriptive statistics. RESULTS: Of the 511 respondents, 88.5% (452/511) searched for medical information either daily or several times per week. The most common questions involved dosing and administration, drug-drug interactions, adverse events and safety, clinical practice guidelines, and disease state information. The main rationale for using specific medical websites or apps and general online search engines frequently or very frequently was ease of use (medical websites or apps: 269/356, 75.6%; general online search engines: 248/284, 87.3%). Accuracy was the main rationale for frequent or very frequent use of medical literature search databases (163/245, 66.5%), prescribing labels or information (122/213, 57.3%), and professional literature (120/195, 61.5%). The main reason for rarely or never using specific medical websites or apps and medical literature search databases was unfamiliarity (medical websites or apps: 16/48, 33%; medical literature search databases: 35/78, 45%); for general online search engines, inaccuracy (34/54, 63%); and for prescribing labels or information and professional literature, excessive time (prescribing labels or information : 54/102, 52.9%; professional literature: 66/106, 62.3%). The pharmaceutical company was sometimes used as a resource for medical information. When the medical information department was used, the call center and the website were considered thorough and complete (call center: 14/25, 56%; website: 33/55, 60%). However, the rationale for not using the call center was the time required (199/346, 57.5%) and the website being unfamiliar (129/267, 48.3%). CONCLUSIONS: The driving forces in the selection of resources are accuracy and ease of use. There is an opportunity to increase awareness of all the appropriate resources for HCPs which may aid in their daily clinical decisions. Specifically, pharmaceutical company medical information departments can help fulfill this need by addressing two major challenges with use of the pharmaceutical company: lack of awareness of medical information services and the speed at which responses are disseminated. Overall, there is lack of understanding or appreciation of the range of pathways to obtain published information and knowledge from pharmaceutical company medical information services. Among the many challenges resource champions will face are the ability to effectively make resources and their platforms accessible, known, and useful to the scientific community.
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 enseignantsNi 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.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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.
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 source (Gemma direct ou Codex distillé), 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 ».