Sharing individual participant data from clinical studies: a cross-sectional online survey among Italian patient and citizen groups
Bibliographic record
Abstract
OBJECTIVES: To gather knowledge on the current debate, opinions and attitudes of Italian patient and citizen groups on individual participant data (IPD) sharing from clinical studies. DESIGN: Cross-sectional online survey. SETTING AND PARTICIPANTS: A 22-item online questionnaire was sent by email to 2003 contacts of patient and citizen groups in Italy. We received 311 responses, checked for duplicate respondents (16); 295 single groups responded, 280 providing questionnaires eligible for analysis (response rate 15%). Ninety (32.1%) dealt with oncology and palliative care, 175 (46.2%) operated locally or regionally and 136 (48.6%) were involved in clinical research. OUTCOME MEASURE: Data on Italian patient and citizen groups' self-reported knowledge, attitudes and opinions on IPD sharing, mechanisms for IPD access, advantages and risks. RESULTS: Half the respondents (144 out of 280, 51%) had some knowledge about the IPD sharing debate, and 60 (42%) stated they had an official position (35 in favour, 19 in favour with restrictions, 2 against, 1 neither for nor against, 3 missing). Nineteen discussed the topic encouraged by this survey; 39% approved broad access by researchers and other professions and identified information to participants, data de-identification, secure archives, access agreements and sanctions for misuse as important aspects of IPD sharing models. Respondents highlighted re-identification, privacy and re-use of data for purposes that participants do not agree on, as main risks, advancement of innovation and reducing waste in research as main advantages. Around half believed IPD sharing would not discourage study participation. CONCLUSIONS: Half the respondents were aware of the debate. Those who had an official position were mainly in favour of IPD sharing. Many supported broad access, asking for conditions important for building trust in entities that handle IPD sharing.Although limited by the low response rate, these findings reinforce the demand for reliable and transparent processes where accountabilities are clear.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.025 | 0.059 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".