Community health fair with follow‐up
Notice bibliographique
Résumé
Medical and health professions schools commonly host community health fairs staffed by students. Fairs typically provide patrons with free health screenings and health education. These events allow students opportunities to practise health promotion, while forging relationships with their local communities. Although health fairs appear to offer patrons many benefits with little risk for harm, these events nevertheless require review to ensure they are established ethically and that their benefits outweigh any risks.1 Potential risks include the instigation of expensive tests indicated by false positive screenings and the delivery of false negative results. As our institution's annual health fair in a historically medically underserved community grew in both attendance and scope, we hoped to ensure our patrons benefited from the services we provide. Unfortunately, assessment of health fair successes in the literature is largely limited to measures of outputs (e.g. number of people screened), and rarely are actual health outcomes following fairs reported. We decided to investigate whether our health fair was ethically and sustainably implemented to promote tangible benefits. Student leaders of our annual health fair developed an institutional review board-approved health awareness programme (HAP) to establish follow-up contact with fair patrons. The HAP committee recruits medical and health professions students throughout the year, and these students receive monthly training in health promotion and disease prevention. At the fair, these volunteers identify patrons at increased risk for adverse cardiovascular events through self-report surveys and connect them with local health care providers and community clinics. These patrons undergo an informed consent process to protect their autonomy and privacy. HAP volunteers then contact study subjects quarterly to track their progress in finding access to medical care and to collect information about outcomes following the fair. Patrons who have not visited a doctor or clinic are offered more information and assistance in accessing medical services. Ultimately, the goal of the HAP is to sustain data-driven follow-up that expands the impact of our annual fair. Through HAP follow-up, we have learned that our fair can have a measurable, positive impact on members of the community it serves. Data from our most recent fair revealed that after 1 month 30% of patrons had already scheduled a doctor's appointment and 65% planned to schedule one. However, nearly 75% of those planning to schedule an appointment wanted help in finding a clinic and making an appointment. Although patrons had been given information about scheduling appointments at community clinics on the day of the fair, the majority requested additional help 1 month later. After 6 months, 65% of those contacted had visited a doctor, and 57% of those who had done so had been given a new diagnosis. Without HAP follow-up, many patrons might never have visited a doctor after the fair. The HAP committee will continue to improve this model to ensure patrons screened at the fair successfully establish continuity of care.
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 | 0,022 | 0,061 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,001 |
| Études des sciences et des technologies | 0,007 | 0,001 |
| Communication savante | 0,003 | 0,003 |
| Science ouverte | 0,003 | 0,009 |
| Intégrité de la recherche | 0,002 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,130 | 0,014 |
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 ».