MétaCan
Menu
Back to cohort
Record W2252789829 · doi:10.1108/ijphm-06-2014-0033

Do consumers perceive their doctors as influenced by pharmaceutical marketing communications?

2015· article· en· W2252789829 on OpenAlexaff
Mei‐Ling Wei, Marjorie Delbaere

Bibliographic record

VenueInternational Journal of Pharmaceutical and Healthcare Marketing · 2015
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsUniversity of SaskatchewanSaint Mary's University
Fundersnot available
KeywordsPersuasionSkepticismMarketingPsychologyOriginalityPharmaceutical marketingAdvertisingValue (mathematics)Sample (material)Marketing researchPharmaceutical industryBusinessMedicineSocial psychologyComputer science

Abstract

fetched live from OpenAlex

Purpose – This paper aims to explore whether and how consumers perceive the impact of pharmaceutical marketing on their own doctor’s prescribing behaviors, and subsequent responses toward their doctor’s advice. Design/methodology/approach – Three experimental studies were conducted. Studies 1 and 2 are based on text-based manipulations and undergraduate student research participants. Study 3 uses image-based manipulations and average adult consumers. Findings – Study 1 demonstrates that consumers can be quite skeptical about their doctor’s motives for prescribing certain brand-name drugs; in particular, consumers can construe doctors as agents of persuasion for prescribed brands. Study 2 shows that this can result not only in choosing generic drugs over prescribed brands but also in opting out of pharmaceuticals altogether by choosing alternatives like natural remedies. Study 3 further demonstrates that these effects can be easily triggered by visual cues in a non-student sample. Originality/value – This research builds on the existing literature on pharmaceutical marketing communications, and extends the theory of persuasion knowledge into healthcare settings.

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)0.013
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0010.000

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 head0.393
GPT teacher head0.573
Teacher spread0.180 · 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 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

Citations11
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Pharmaceutical and Healthcare MarketingSame topicPharmaceutical industry and healthcareFrench-language works237,207