Notice bibliographique
Résumé
The subject of mortality is a meeting ground for diverse disciplines, and hence it is not surprising to see it being approached from remarkably different perspectives.The book has 13 chapters, covering a wide range of topics, using data from different geographic areas.The chapters can be grouped into three broad themes: mortality estimation and projections (chapters 2, 3, and 5), explanation of trends in mortality and causes of death (chapters 4, 8, 9, 11, and 13), and measurement of impact of determinants (chapters 6, 7, 10, and 12).Chapter 1, by Jon Anson and Marc Luy, adequately summarizes all the chapters in the book.It is clear that the aim of the book is to put together 'state of the art' methods used in mortality and morbidity.However, it is in the chapters dealing with mortality estimation and projections that "cutting edge" methods are used.The remaining chapters, interesting as they are, used methods that would fall under "normal science" rather than cutting-edge methods.Chapter 2 is authored by Peter Congdon, a pioneer in the analysis of small area mortality and the author of books on Bayesian statistical modelling.In this chapter, he exploited correlations between adjacent ages and areas with Bayesian modelling and applied it to data of over 3,000 US counties.He found that "whereas there is little gain in life expectancy in the lowest income counties, high income counties showed expectancy improvements exceeding the US average."This new approach is an improvement on standard conventional life table methods used in small area mortality that overlook spatial or age correlations.Chapter 3, by Joroen Spijker, is clearly the most ambitious chapter in the book.He uses data from 21 countries over the period from 1980 to 2000 to model death rates for 11 causes of death.The model used allows for the simulataneous analysis of inter-country and inter-temporal variations in mortality.As a departure from other models based on extrapolation, this model included data on some known socioeconomic determinants of mortality.The model was validated and then used to produce short-term projections of rates due to causes of death.This is a significant contribution in an area that is still in its youthful stage of development.Chapter 4, by Katalin Kovács, thoroughly reviews the different variants of Epidemiological Transition Theory and the Nutritional Transition Theory.Using causes of death data from Hungary, Kovács tries to group the different causes of death in such a way as to allow her to see the role of the different theories in explaining inequalities in mortality between the less educated and the more educated.Her conclusion was that "nutrition transition theory provides a very plausible explanatory framework for the growth of mortality inequalities."Chapter 5, by Sarinapha Vasunilashorrn and others, attempts to predict mortality from profiles of biological risk and performance measures of functioning.They were able to get a rich set of data by linking a national US survey data with the causes of death data contained in the National Death Index.According to the authors,
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,005 | 0,004 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,008 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,009 | 0,002 |
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 ».