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Relationship between paediatricians and the pharmaceutical and commercial milk formula industries: an explanatory mixed methods study

2025· article· en· W4415703238 on OpenAlexaff
Mario Alejandro Leon-Ayala, Maria Alejandra Agudelo-Velasquez, Carlos Enrique Yepes Delgado, Iván D. Flórez

Bibliographic record

VenueArchives of Disease in Childhood · 2025
Typearticle
Languageen
FieldPharmacology, Toxicology and Pharmaceutics
TopicPharmaceutical industry and healthcare
Canadian institutionsMcMaster University
Fundersnot available
KeywordsInvisibilityMEDLINEPublic healthInfant formulaAlternative medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: To describe and understand relationships between paediatricians and industry in Colombia. METHODS: Mixed methods study composed of three phases: quantitative (cross-sectional study, e-survey to paediatricians) describing interaction patterns and evaluating the factors that explain the perceived need for industry visits to health services and their influence through multivariate analyses; qualitative (grounded theory study, semistructured interviews) to create an explanatory theory; and an integrative phase (mixed methods explanatory study). RESULTS: We surveyed 218 paediatricians (mean age, 45.2 years), nearly all of whom interacted with industry monthly, mainly with the commercial milk- formula industry (CMFI). Sponsored trips to attend conferences and not having teaching activities were associated with a perception of the potential effects of the trips on prescribing practices. Moreover, being sponsored for an international trip was associated with the perception of indispensability of industry visits for maintaining continuing medical education (CME). In the qualitative phase, four phenomena emerged; two related to the quantitative findings: 'Paediatrician's dependence on industry for CME', and 'Colombian health context facilitates a closer relationship between paediatricians and industry'. Two emerged as explaining factors: 'Normalisation of paediatrician-industry interaction' and 'Paediatrician's prescriptive power: mediator between their conflict of interest with industry and patients'. The integration showed how the qualitative phase explained the quantitative findings. CONCLUSIONS: Most paediatricians frequently interact with industry, mainly with the CMFI, and this is perceived as necessary for CME. The normalisation of the paediatrician-industry interaction leads to the underestimation and invisibility of its effects, which fosters the notion of its necessity for the paediatric field.

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)0.006
metaresearch head score (Gemma)0.010
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.264
GPT teacher head0.554
Teacher spread0.290 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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