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Record W4366179311 · doi:10.34172/ijhpm.2023.7916

Coloniality, Elite Networks and Intersectionality: Key Concepts in Understanding Biomedical Power and Equity in Health Policy Processes Comment on "Power Dynamics Among Health Professionals in Nigeria: A Case Study of the Global Fund Policy Process"

2023· letter· en· W4366179311 on OpenAlexaff
Rakesh Parashar, Veena Sriram, Sharmishtha Nanda, Frayashti Shekhawat

Bibliographic record

VenueInternational Journal of Health Policy and Management · 2023
Typeletter
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Public Health Policies and Epidemiology
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsIntersectionalityEquity (law)ElitePower (physics)Global healthPolitical scienceSociologyKey (lock)Dynamics (music)Health professionalsPublic relationsEconomic growthEconomicsGender studiesHealth carePoliticsComputer science

Abstract

fetched live from OpenAlex

To understand the role of power in health policy processes in low- and middle-income country (LMIC) contexts, it is necessary to engage with global and local power structures and their historical contexts. In this commentary, we outline three dimensions that shape a dominant power in health policy processes-the biomedical power. We propose that understanding the linkages between medical power and colonialism; the close connection of public health, medicine and elite networks; and the intersectionalities that shape the powers of medical professionals can offer the means to examine the biomedical hegemony in health policy processes. Additionally we suggest that a more nuanced understanding of the interaction of local powers with global funding can offer some entry points to achieving more equitable and interdisciplinary health policy processes in LMICs.

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)0.012
metaresearch head score (Gemma)0.027
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: Commentary · Consensus signal: Commentary
Teacher disagreement score0.043
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.027
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0160.020
Scholarly communication0.0090.011
Open science0.0030.005
Research integrity0.0430.037
Insufficient payload (model declined to judge)0.0040.001

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 head0.104
GPT teacher head0.474
Teacher spread0.370 · 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
GenreCommentary

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

Citations1
Published2023
Admission routes1
Has abstractyes

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