Amélioration de la communication en médecine générale autour de la sexualité des patients âgés de plus de 65 ans : élaboration d’un outil d’aide basé sur un questionnaire
Bibliographic record
Abstract
Introduction: The aging population in France currently exceeds 14 million people and is expected to represent over a quarter of the population by 2040. Despite an increase in healthy life expectancy, the issue of sexuality among the elderly is often overlooked. The National Sexual Health Strategy 2017-2030 aims to address the needs of vulnerable populations, including the elderly, by encouraging research and innovation in this field. It is essential to understand the sexuality of the elderly and to take their approach preferences into account. Objective: This study seeks to create a dialogue tool to discuss about sexuality in general medical consultations for people aged 65 and over, focusing on the most relevant topics according to the target population. Material: A descriptive quantitative and multicenter observational study conducted in Savoie and Haute-Savoie over a period of 5 months, involving people over 65 years old. A questionnaire consisting of 19 questions was used to assess the understanding, relevance, and discomfort associated with each question. Data were collected and analyzed in accordance with confidentiality standards. The Khi-2 test was used for questions identified as relevant. Second questionnaire consisting of 9 questions aimed at general practitioners, exploring their perspectives on this subject and soliciting their opinions on a discussion aid tool. Results: 43.5% of the 1000 distributed questionnaires were returned, with 426 questionnaires analyzed. Questions considered relevant by more than 2/3 of the population concerned sexually transmitted infections (STI), experiences of violence, and the effects of medication on sexuality. Flyers and leaflets were seen as a potential solution to facilitate dialogue. Questions addressing the psychological aspects of sexuality were deemed embarrassing in most cases. The majority of general practitioners (65%) had initiated discussions on sexuality with their patients and felt justified in doing so. Approximately 80% were supportive of implementing a self-service questionnaire to facilitate dialogue on this topic. Conclusion: Three themes are relevant to be included in a new tool to discuss about sexuality with the general practitioner: information on STIs/STDs, screening for potential violence, and reporting adverse effects of medications on sexuality.
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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 | 0.030 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".