Health risk appraisal for older people 4: case finding for hypertension, hyperlipidaemia and diabetes mellitus in older people in English general practice before the introduction of the Quality and Outcomes Framework
Notice bibliographique
Résumé
BACKGROUND: Early intervention can help to reduce the burden of disability in the older population, but many do not access preventive care. There is uncertainty over what factors influence case finding in older patients in general practice. AIM: To explore factors associated with case finding for hypertension, hyperlipidaemia and diabetes mellitus in older patients. METHOD: Two thousand four hundred and ninety-one patients aged 65 years and above were recruited from three large practices in suburban London before the introduction of the Quality and Outcomes Framework (QOF) completed a questionnaire on health, functional status, health behaviours and preventive care. FINDINGS: Those not reporting heart disease, diabetes or hypertension were included in a secondary data analysis to explore factors influencing uptake of preventive care measures. Approximately one-third denied having had a blood pressure check in the previous year. They were more likely to have had little contact with doctors and to have an unhealthy lifestyle (smoking and a high-fat diet). One-third reported a cholesterol test in the previous five years. Cholesterol measurement was reported more often by men and those with a high body mass index. Those with unhealthy lifestyles (smoking and high-fat diet), those who had only received the state pension and those who limited their activities because of a fear of falling were less likely to report cholesterol measurement. About 10% reported a fasting blood glucose measurement and were more likely to consult more often and have more medications, but they were less likely to have a high-fat diet. Preventive care uptake was associated with frequent contacts with doctors, but overall the uptake of preventive care was low. Older people with healthier lifestyles were more likely to have primary preventative care interventions. These findings provide a baseline against which the effect of the QOF on the care of older people can be measured in future studies.
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,023 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 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 tête enseignante, 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 ».