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Record W3037856755 · doi:10.22215/etd/2020-14110

'Superbugs' and the 'Dirty Hospital': The Social Co-Production of Public Health Risks

2020· dissertation· en· W3037856755 on OpenAlexaff
Gabriela Capurro

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldSocial Sciences
TopicRisk Perception and Management
Canadian institutionsCarleton University
Fundersnot available
KeywordsBlamePublic relationsNegotiationSituational ethicsPolitical scienceRisk managementSociologyBusinessSocial psychologyPsychologySocial science

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.024
metaresearch head score (Gemma)0.032
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.979
Threshold uncertainty score0.127

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0240.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.002
Science and technology studies0.0210.081
Scholarly communication0.0180.021
Open science0.0020.024
Research integrity0.0070.007
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.106
GPT teacher head0.417
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2020
Admission routes1
Has abstractyes

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