'Superbugs' and the 'Dirty Hospital': The Social Co-Production of Public Health Risks
Notice bibliographique
Résumé
This dissertation examines the construction of antimicrobial resistance (AMR) as a public health risk.Its focus is on how AMR is co-produced among a network of medical professionals, scientists, and science journalists.The research advances three main arguments: first, narratives and definitions of health risk are not absolute or fixed, but constituted in the discourses and practices of global, national and local actors; second, the production of knowledge about the risk of AMR is not based on a linear process but one in which various definitions, interests, and practices are involved, and influence one another; and third, conceptualizing health risk as discursive co-production provides a more robust and nuanced understanding of how risks are defined and understood by stakeholders, particularly in relation to attributions of responsibility, blame, victimhood, and resource allocation.I argue that this represents a novel way of imagining and conceptualizing risk communication.The research involved the development of a novel methodology, which I call ethnography of risk, that brings together hospital ethnography, in-depth interviews, and qualitative analysis of media coverage and policy documents.The results of this study show that health risks are co-produced through processes of negotiation between different and co-existing types of knowledge, including situational and embodied experience, emotional memory, and expert assessments.Second, it argues that risks are multifaceted and constituted at the intersection of different perspectives, such that AMR is understood and addressed as a personal risk, a professional risk, a global risk, and a political risk.Third, it shows that stakeholders perform boundary work and blame shifting to justify why they preferred certain ways of knowledge over others.Fourth, various stakeholders reified, and in that sense co-produced, the deficit model of risk communication through narratives and actions that keep creating the conditions in which the supposed knowledge deficit is iii circulated.Finally, AMR lacks a compelling narrative and is communicated as abstract, lost in a plethora or other, more urgent risks.These results open up new ways of conceptualizing health risks beyond the biomedical model and emphasize the need for studies in risk communication and health communication that critically examine the actors, sites and processes that produce and circulate risk knowledge.guidance of great mentors, and the encouragement of my colleagues, friends, and family.Thank you to my supervisor, Dr. Josh Greenberg, for your mentorship, guidance, and patience.For your insightful comments and invaluable advice on many aspects of academic life, for inviting me to collaborate with you on research projects, and for being generous with your time and critical review of my work.You always believed in this project and helped me find the way to make it happen; even when things got hard and there seemed to be no way out you remained optimistic.I would also like to thank my committee members, Dr. Sheryl Hamilton and Dr.Chris Russill for their support and guidance.You always had your door open to discuss ideas and you offered me new and creative perspectives.Thank you for persuading me to be more daring and for believing I could do
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 | 0,024 | 0,032 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,021 | 0,081 |
| Communication savante | 0,018 | 0,021 |
| Science ouverte | 0,002 | 0,024 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 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 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 ».