The state of prevention in allied health education and practice.
Notice bibliographique
Résumé
HEALTH CARE COSTS, representing 13.3% of the U.S. gross domestic product in and 14.1% in 2001, remain a major barrier to providing adequate access to care. Escalating costs have been fueled primarily by spending for prescription drugs (15.7%), hospital services (8.3%), and physician and clinical services (8.6%),1 most of which are likely to be associated with illness care. Outcomes of care in the United States do not compare favorably with other countries committing similar resources to health care. The United States spent $4,000 per person in 1998 compared to $2,860 by Switzerland, the nation that ranked second in health care spending. Yet Americans lived an average of 77 years compared with the Swiss life expectancy of 80 years. Similarly, Canada and Japan, both with average life expectances of 81, spent $2,363 and $1,763 per person. There has been some benefit to the increased U.S. spending because the average life expectancy has increased from 70 to 77 years over the past 4 decades; however, the United States still falls behind other industrialized countries in the cost of health care relative to longevity of the population.2 Prevention (health promotion and risk reduction) has been touted as an important strategy to reduce health care costs. The underlying principle of such an approach is that the cost of prevention or early treatment would be less than the cost of aggressively treating a serious illness. Prevention can be accomplished only if all health care providers, regardless of discipline, integrate health promotion/risk reduction into their practice. This article reviews the state of prevention in allied health education and practice and discusses the experience of a College of Health Sciences and Human Services in encouraging integration of prevention into allied health practice. Emphasis on Prevention Since the 1970s, there have been significant efforts from governments and a variety of health care advocates to integrate prevention into health care. In 1979, the SurgeonGeneral's reports, Healthy and Healthy People 2000: National Health Promotion and Disease Prevention, established national health objectives that served as a basis for the development of state and community plans.3 Under the direction of the U.S. Public Health Services, the Healthy People 2000 goals have been replaced with Healthy People 2010 goals, revised to include (1) increasing life expectancy and improving quality of life and (2) eliminating health disparities among the U. S. population.4 Prevention also is receiving increased attention internationally. The objective of the Department of Noncommunicable Disease Prevention and Health Promotion of the World Health Organization is to reduce the incidence of noncommunicable diseases and promote positive health and well-being, with particular focus on developing countries. The strategy to reach this objective is to emphasize major risk factors for noncommunicable diseases and the underlying determinants of health.5 Similarly the goal of the PanAmerican Health Organization includes promotion of primary health care and expediting health promotion to help countries deal with health problems typical of development and urbanization, such as cardiovascular diseases, cancer, accidents, smoking, and addiction to drugs and alcohol.6 SPECIFIC GCWERNMENTAL INITIATIVES In 1984, the U.S. Public Health Services established the United States Preventive Health Service Task Force (USPHSTF). This independent panel of experts in primary care and prevention, now under the auspices of the Agency for Healthcare Research and Quality (AHRQ), reviews evidence of effectiveness and develops recommendations for clinical preventive services. Similarly, the Centers for Dis ease Control and Prevention (CDC) sponsor a task force on community preventive services and publish the Guide to Community Preventive Services.7 Both initiatives are designed to determine under what circumstances prevention/risk reduction strategies should be implemented and the standard of care for each intervention. …
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 | 0,007 | 0,015 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,006 | 0,005 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,005 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,081 | 0,029 |
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 ».