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Record W1969085579 · doi:10.1177/1049732314568199

Media Discourse on the Social Acceptability of Fecal Transplants

2015· article· en· W1969085579 on OpenAlexafffundabout
Kim Chuong, Kieran C. O’Doherty, David Secko

Bibliographic record

VenueQualitative Health Research · 2015
Typearticle
Languageen
FieldMedicine
TopicClostridium difficile and Clostridium perfringens research
Canadian institutionsConcordia UniversityUniversity of Guelph
FundersCanadian Institutes of Health Research
KeywordsFecal bacteriotherapyMicrobiomeClostridium difficileLegitimacyFecesMedicineImmunologyBiologyPolitical scienceMicrobiologyBioinformaticsAntibiotics

Abstract

fetched live from OpenAlex

Advances in human microbiome research have generated considerable interest in elucidating the role of bacteria in health and the application of microbial ecosystem therapies and probiotics. Fecal transplants involve the introduction of gut microbes from a healthy donor's stool to the patient and have been documented as effective for treating Clostridium difficile infections (CDIs) and some other gastrointestinal disorders. However, the treatment has encountered regulatory hurdles preventing widespread uptake. We examined dominant representations of fecal transplants in Canadian media and found that fecal transplants are often represented as being inherently disgusting or distasteful (the "ick factor"). This "ick factor" is used to construct different messages about the treatment's social acceptability and legitimacy. We conclude that an over-emphasis on the "ick factor" constrains public discourse from a more nuanced discussion of the social challenges, scientific concerns, and regulatory issues surrounding the treatment.

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)0.026
metaresearch head score (Gemma)0.060
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.177
Threshold uncertainty score0.352

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0260.060
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.003
Science and technology studies0.0220.032
Scholarly communication0.0100.007
Open science0.0010.008
Research integrity0.0030.005
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 head0.639
GPT teacher head0.624
Teacher spread0.015 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations23
Published2015
Admission routes3
Has abstractyes

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