A European Network of Email and Telephone Help Lines Providing Information and Support on Rare Diseases: Results From a 1-Month Activity Survey
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".