The Primary Care and Environmental Health e-Learning Course to Integrate Environmental Health in General Practice: Before-and-After Feasibility Study
Notice bibliographique
Résumé
Background Environmental and behavioral factors are responsible for 12.6 million deaths annually and contribute to 25% of deaths and chronic diseases worldwide. Through the One Health initiative, the World Health Organization and other international health organizations plan to improve these indicators to create healthier environments by 2030. To meet this challenge, training primary care professionals should be the priority of national policies. General practitioners (GPs) are ready to become involved but need in-depth training to gain and apply environmental health (EH) knowledge to their practice. In response, we designed the Primary Care Environment and Health (PCEH) online course in partnership with the Occitanie Regional Health Agency in France. This course was used to train GP residents from the Montpelier-Nimes Faculty of Medicine in EH knowledge. The course was organized in 2 successive parts: (1) an asynchronous e-learning modular course focusing on EH knowledge and tools and (2) 1 day of face-to-face sessions. Objective This study assessed the impact of the e-learning component of the PCEH course on participants’ satisfaction, knowledge, and behavior changes toward EH. Methods This was a pilot before-and-after study. Four modules were available in the 6-hour e-learning course: introduction to EH, population-based approach (mapping tools and resources), clinical cases, and communication tools. From August to September 2021, we recruited first-year GP residents from the University of Montpellier (N=130). Participants’ satisfaction, knowledge improvements for 19 EH risks, procedure to report EH risks to health authorities online, and behavior change (to consider the possible effects of the environment on their own and their patients’ health) were assessed using self-reported questionnaires on a Likert scale (1-5). Paired Student t tests and the McNemar χ2 test were used to compare quantitative and qualitative variables, respectively, before and after the course. Results A total of 74 GP residents completed the e-learning and answered the pre- and posttest questionnaires. The mean satisfaction score was 4.0 (SD 0.9) out of 5. Knowledge scores of EH risks increased significantly after the e-learning course, with a mean difference of 30% (P<.001) for all items. Behavioral scores improved significantly by 18% for the participant’s health and by 26% for patients’ health (P<.001). These improvements did not vary significantly according to participant characteristics (eg, sex, children, place of work). Conclusions The e-learning course improved knowledge and behavior related to EH. Further studies are needed to assess the impact of the PCEH course on clinical practice and potential benefits for patients. This course was designed to serve as a knowledge base that could be reused each year with a view toward sustainability. This course will integrate new modules and will be adapted to the evolution of EH status indicators and target population needs.
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 | 0,014 | 0,011 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,002 | 0,002 |
| Bibliométrie | 0,002 | 0,001 |
| Études des sciences et des technologies | 0,004 | 0,002 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».