Notice bibliographique
Résumé
As currently conceived, precision public health is at risk of becoming precision medicine at a population level. This paper outlines a framework for precision public health that, in contrast to its current operationalisation, is consistent with public health principles because it integrates factors at all levels, while illuminating social position as a fundamental determinant of health and health inequities. We review conceptual foundations of public health, outline a proposed framework for precision public health and describe its operationalisation within research and practice. Social position shapes individuals' unequal experiences of the social determinants of health. Thus, in our formulation, precision public health investigates how multiple dimensions of social position interact to confer health risk differently for precisely defined population subgroups according to the social contexts in which they are embedded, while considering relevant biological and behavioural factors. It leverages this information to uncover the precise and intersecting social structures that pattern health outcomes, and to identify actionable interventions within the social contexts of affected groups. We contend that studies informed by this framework offer greater potential to improve health than current conceptualisations of precision public health that do not address root causes. Moreover, expanding beyond master categories of social position and operationalising these categories in more precise ways across time and place can enrich public health research through greater attention to the heterogeneity of social positions, their causes and health effects, leading to the identification of points of intervention that are specific enough to be useful in reducing health inequities. Failure to attend to this level of particularity may mask the true nature of health risk, the causal mechanisms at play and appropriate interventions. Conceptualised thus, precision public health is a research endeavour with much to offer by way of understanding and intervening on the causes of poor health and health inequities.As currently conceived, precision public health is at risk of becoming precision medicine at a population level. This paper outlines a framework for precision public health that, in contrast to its current operationalization, is consistent with public health principles because it integrates factors at all levels, while illuminating social position as a fundamental determinant of health and health inequities. We review conceptual foundations of public health, outline a proposed framework for precision public health and describe its operationalization within research and practice. Social position shapes individuals' unequal experiences of the social determinants of health. Thus, in our formulation, precision public health investigates how multiple dimensions of social position interact to confer health risk differently for precisely defined population subgroups according to the social contexts in which they are embedded, while considering relevant biological and behavioural factors. It leverages this information to uncover the precise and intersecting social structures that pattern health outcomes, and to identify actionable interventions within the social contexts of affected groups. We contend that studies informed by this framework offer greater potential to improve health than current conceptualizations of precision public health that do not address root causes. Moreover, expanding beyond master categories of social position and operationalizing these categories in more precise ways across time and place can enrich public health research through greater attention to the heterogeneity of social positions, their causes and health effects, leading to identification of points of intervention that are specific enough to be useful in reducing health inequities. Failure to attend to this level of particularity may mask the true nature of health risk, the causal mechanisms at play and appropriate interventions. Conceptualized thus, precision public health is a research endeavour with much to offer by way of understanding and intervening on the causes of poor health and health inequities.
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 | 0,152 | 0,109 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,002 |
| Méta-épidémiologie (sens large) | 0,005 | 0,003 |
| Bibliométrie | 0,008 | 0,005 |
| Études des sciences et des technologies | 0,008 | 0,144 |
| Communication savante | 0,028 | 0,036 |
| Science ouverte | 0,007 | 0,033 |
| Intégrité de la recherche | 0,019 | 0,026 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,001 |
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 ».