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Record W2302448639 · doi:10.1515/irsr-2013-0012

Building Business Relationships Through the Web: How Medical Technology Companies Enroll Stakeholders in Innovation Development and Uptake

2013· article· en· W2302448639 on OpenAlexafffundabout
Myriam Hivon, Pascale Lehoux, Christopher J. Longo, Bryn Williams–Jones, Fiona A. Miller

Bibliographic record

VenueInternational Review of Social Research · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicPublic Relations and Crisis Communication
Canadian institutionsUniversité de MontréalMcMaster UniversityUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsCommercializationScope (computer science)BusinessPublic relationsHealth careSpace (punctuation)MarketingThe InternetPolitical science

Abstract

fetched live from OpenAlex

Abstract: Websites are perceived as an additional communication space where public and private institutions and their stakeholders can interact and develop sustainable relationships. Although public relations scholars argue that both companies and consumers may benefit from virtual interactions, the growing online direct-to-consumer advertising and sale of health-related products has raised social and ethical concerns. Our study seeks to clarify the scope and nature of the virtual relationships that are specific to medical devices companies. Through a qualitative analysis of website, we show how four Canadian medical technology companies sought to enroll three types of stakeholders into their innovation development and commercialization strategies: investors, healthcare providers and patients. Our findings show that by reinforcing stereotypical relationships with investors, the websites maintain certain disconnect between the worlds of business and healthcare, and by creating proactive roles for healthcare providers and patients, they contribute to forge ethically convoluted relationships.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.844
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.305
GPT teacher head0.472
Teacher spread0.167 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations0
Published2013
Admission routes3
Has abstractyes

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