Analysis on the Perceptions Toward Mild Cognitive Impairment and Medical Willingness among Population Aged over 55 Years in Shanghai Based on a Proactive Health Perspective
Notice bibliographique
Résumé
Background Proactive health is an important measure to implement the Healthy China strategy. Mild cognitive impairment (MCI) is an important breakthrough point for early detection and intervention of cognitive impairment disorders and it is also a key link in the realization of brain health. Objective To activate the initial health intervention among community population and fully realize the construction of a healthy China by understanding the perceptions and medical willingness among community populations aged over 55 years in Shanghai. Methods From October to December 2021, one district of Shanghai's urban and suburban areas was randomly selected (Yangpu District for the urban area and Jiading District for the suburban area), and 1-2 community health service centers were randomly selected from each district (Daqiao Community Health Service Center and Dinghai Community Health Service Center for Yangpu District, and Jiading Town Community Health Service Center for Jiading District). An on-site face-to-face questionnaire survey was conducted among the residents waiting for outpatient consultation at the community health service centers in accordance with the inclusion criteria. The content of community populations' perceptions questionnaire included: (1) general demographic characteristics; (2) the level of MCI disease awareness among the community population; (3) the medical willingness of the community population. Logistic regression analysis was used to explore the factors influencing the medical willingness of the community population. Results A total of 970 questionnaires were distributed and 951 valid questionnaires were recovered, with a valid recovery rate of 98.04%. (1) The total score of the community populations' perceptions questionnaire for MCI was (14.55±5.24), 51.3% (488/951) of the community populations were aware of "mild cognitive impairment", mainly through the media (61.7%, 301/488) ; 59.9% (570/951) of the populations believede that "mild cognitive impairment occurs in old age"; 14.1% (134/951) of the population had participated in relevant screening activities; 6.2% (59/951) had consulted a doctor for memory impairment or suspected cognitive impairment. (2) Univariate and multivariate analysis showed that family history of cognitive impairment, knowledge and understanding of MCI as well as personal experience were all influencing factors of community populations' medical willingness for MCI. Conclusion Community population aged over 55 years have poor MCI disease perceptions and poor medical willingness. The community populations with poor knowledge, biased understanding of MCI and lack of relevant practical experience had poor medical willingness. It is suggested that multi-angle publicity should be carried out to improve the perceptions of MCI disease in the community and provide comprehensive support, to improve the accessibility of proactive health, and explore effective ways to promote proactive health.
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 | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
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 ».