A European Network of Email and Telephone Help Lines Providing Information and Support on Rare Diseases: Results From a 1-Month Activity Survey
Notice bibliographique
Résumé
BACKGROUND: Information on rare diseases are often complex to understand, or difficult to access and additional support is often necessary. Rare diseases helplines work together across Europe to respond to calls and emails from the public at large, including patients, health care professionals, families, and students. Measuring the activity of helplines can help decision makers to allocate adequate funds when deciding to create or expand an equivalent service. OBJECTIVE: Data presented are referred to a monthly user profile analysis, which is one of the activities that each helpline has to carry out to be part of the network. This survey aimed to explore the information requests and characteristics of users of rare diseases helplines in different European countries. Another aim was to analyze these data with respect to users' characteristics, helpline characteristics, topics of the inquiries, and technologies used to provide information. With this survey, we measure data that are key for planning information services on rare diseases in the context of the development of national plans for rare diseases. METHODS: A survey was conducted based on all calls, emails, visits, or letters received from November 1 to 30, 2012 to monitor the activity represented by 12 helplines. Data were collected by a common standardized form, using ORPHA Codes for rare diseases, when applicable. No personal data identifying the inquirer were collected. It was a descriptive approach documenting on the number and purpose of inquiries, the number of respondents, the mode of contact, the category of the inquirer in relation to the patient, the inquirer's gender, age and region of residence, the patient's age when applicable, the type and duration of response, and the satisfaction as scored by the respondents. RESULTS: A total of 1676 calls, emails, or letters were received from November 1 to 30, 2012. Inquiries were mostly about specific diseases. An average of 23 minutes was spent for each inquiry. The inquirer was a patient in 571/1676 inquiries (ie, 34.07% of all cases; 95% CI 31.8-36.3). Other inquirers included relatives (520/1676, 31.03%; 95% CI 28.9-33.3), health care professionals (354/1676, 21.12%; 95% CI 19.2-23.1), and miscellaneous inquirers (230/1676, 13.72%; 95% CI 12.1-15.4). Telephone remained the main mode of contact (988/1676, 58.95%; 95% CI 56.6-61.3), followed by emails (609/1676, 36.34%; 95% CI 34.0-38.6). The three main reasons of inquiries were to acquire about information on the disease (682/2242, 30.42%; 95% CI 27.8-32.1), a specialized center/expert (404/2242, 18.02%; 95% CI 15.9-19.6), and social care (240/2242, 10.70%; 95% CI 9.1-12.0). CONCLUSIONS: The helplines service responds to the demands of the public, however more inquiry-categories could be responded to. This leaves the possibility to expand the scope of the helplines, for example by providing assistance to patients when they are reporting suspected adverse drug reactions as provided by Directive 2010/84/EU or by providing information on patients' rights to cross-border care, as provided by Directive 2010/24/EU.
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 | 0,004 | 0,009 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 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 ».