MétaCan
Menu
Back to cohort
Record W2076287181 · doi:10.5688/ajpe777142

Report of the AACP Task Force on Patient-Centered Medical Homes and Accountable Care Organizations

2013· article· en· W2076287181 on OpenAlexaboutno aff
Alex J. Adams, David R. Clark, Gary E. DeLander, George E. MacKinnon, Michael J. Malloy, Melissa Somma McGivney, Cary Mobley, Wesley Nuffer, Paige Parsons, Andrea Smesny, Marie Smith, Timothy J. Ives

Bibliographic record

VenueAmerican Journal of Pharmaceutical Education · 2013
Typearticle
Languageen
FieldArts and Humanities
TopicDiscourse Analysis in Language Studies
Canadian institutionsnot available
FundersAgency for Healthcare Research and QualityU.S. Department of Health and Human Services
KeywordsRhetorical questionPublic relationsNoticeHealth careCall to actionAction (physics)PreparednessPsychologyBusinessMarketingPolitical science

Abstract

fetched live from OpenAlex

Dominant understandings of genre-as-form have limited our abilities to perceive health awareness: we recognise, and expect, health awareness campaigns from governmental and non-profit agencies. Inversely, we often fail to recognise, or name, health awareness as such when it comes from other sources, such as commercial marketing or advertisements for products. However, rhetorical genre theory centres attention on action brought about by form and, as such, rhetorical genre provides tools for recognising instances of health awareness often escape our notice. One such example is critical-illness insurance marketing. In this article, I argue that critical-illness insurance marketing draws on the same appeals found in cancer awareness campaigns. Through a comparative analysis, I show that Colorectal Cancer Canada and critical-illness insurance marketing represent unpreparedness, rather than cancer, as the exigence, or the problem to be overcome through public discourse, and as such, share a genre of what I call ‘health awareness as preparedness’.

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)0.067
metaresearch head score (Gemma)0.111
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.118
Threshold uncertainty score0.354

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0670.111
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0080.010
Science and technology studies0.0080.003
Scholarly communication0.0140.011
Open science0.0070.011
Research integrity0.0210.013
Insufficient payload (model declined to judge)0.0310.008

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 head0.012
GPT teacher head0.319
Teacher spread0.307 · 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 designNot applicable
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

Citations18
Published2013
Admission routes1
Has abstractyes

Explore more

Same venueAmerican Journal of Pharmaceutical EducationSame topicDiscourse Analysis in Language StudiesFrench-language works237,207