Keeping Up-To-Date: The San Antonio CATs Initiative
Notice bibliographique
Résumé
One of the most significant challenges facing healthcare providers today is the problem of keeping up-to-date. Oral health knowledge is increasing exponentially (Figure 1), and although there is increased specialization, it is more and more difficult to keep up-to-date in any field. In addition, the useful half-life of biomedical knowledge, the time until half the knowledge in a field becomes obsolete or disproven, is only a few years. The result is that a great percentage of the knowledge gained in dental school is out-dated after only a few years in practice. Figure 1 Increase in oral health knowledge is changing our ability to keep up-to-date The problem of keeping up-to-date has been studied extensively in medicine. The results raise serious concerns. A systematic review of research dealing with time in practice and quality of care (Choudhry et al, 2005) concluded, “Physicians who have been in practice for more years and older physicians possess less factual knowledge, are less likely to adhere to appropriate standards of care, and may also have poorer patient outcomes.” Over one-half (52%) of the 62 studies reviewed found decreasing performance on all outcomes assessed. Similar trends are likely to be found in dentistry. Estimates on the delay in adoption of new knowledge are also of concern. Balas and Boren (2000) reported in their review that the delay in adoption of new biomedical knowledge averaged 17 years. Ironically, some new technologies and procedures of questionable benefit to the patient are very rapidly adopted. The aggressive marketing of products and concepts not adequately tested magnifies the problem of keeping up-to-date. The explosion of new knowledge and short half-life of existing biomedical knowledge also has serious implications for dental education. To keep up-to-date, we estimate that approximately one-half of the clinical curricula of a dental school would need to be changed every seven to ten years. There is evidence that dental schools are not keeping up. For example, Klasser and Greene (2007) reported that 23% of the 53 United States and Canadian dental schools surveyed continued to endorse occlusal adjustment for prevention and treatment of TMD despite opposing conclusions presented in three review articles and despite the conclusions of a 2003 Cochrane Systematic Review (Koh & Robinson, 2003) which stated, “Occlusal adjustment cannot be recommended for the management or prevention of TMD.” Given the rapid development of new knowledge and short half-life in all areas of dentistry, it is likely that much of the curricula of many schools may not be current. In summary, there is evidence that the explosion of new knowledge and the turnover of knowledge have exceeded the capacity of current science transfer mechanisms. Both practitioners and dental schools are facing serious challenges in their efforts to keep up-to-date. New models of education and science transfer must be explored to address the situation, which is projected to worsen with the continued exponential growth of new knowledge and products.
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 | 0,042 | 0,040 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,003 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,011 | 0,008 |
| Science ouverte | 0,005 | 0,014 |
| Intégrité de la recherche | 0,006 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,059 | 0,016 |
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 ».