Connaissance de l’outil DMP par le patient Lot-et-Garonnais
Bibliographic record
Abstract
Introduction: the Electronic Health Record (EHR) is a computerized and secure medical record, accessible on the Internet by the patient and his health professionals. While it appears to be an ideal technological tool for modern medicine, its deployment has encountered many obstacles over the years. The reluctance of health professionals has been studied on several occasions, but the literature remains very poor concerning the patients’point of view. The aim of this work is therefore to assess patients' knowledge of the EHR. Materials and methods: we carried out an observational, quantitative and multicenter study on patients from Lot-et-Garonne. We obtained raw data on EHR by collaborating with the CNAM and CPAM 47 on the one hand and interviewed the patients through a questionnaire on the other hand. Results: 424 questionnaires were included in our study, with a mean age population of 50.8 years and a predominance of women (67%). More than half of the patients had heard of the EHR (53%), but only 23% actually had one. We observe in the latter a lack of knowledge of the tool: only a quarter of patients consult their EHR, 19% do not know who feeds it and 31% do not know its content. Among patients who have never heard of the EHR, 51% think it is an interesting tool. For others, the main barriers to opening the EHR were: the lack of involvement of health professionals, lack of information on the subject and fear of medical confidentiality. Conclusion: despite a still weak deployment (implementation ?), the EHR is a tool of which patients have had a positive perception, and which is increasing in popularity. Our results show a lack of knowledge of the tool, but also of its use. While the multiple adoption barriers identified must be taken into account, therapeutic education may be necessary in addition to facilitate the appropriation of DMP by patients.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".