MétaCan
Menu
Back to cohort
Record W1998676073 · doi:10.1186/1472-698x-9-15

Sex, gender, and health biotechnology: points to consider

2009· article· en· W1998676073 on OpenAlexafffund
Jerome Amir Singh, Sunita V S Bandewar, Peter Singer

Bibliographic record

VenueBMC International Health and Human Rights · 2009
Typearticle
Languageen
FieldMedicine
TopicSex and Gender in Healthcare
Canadian institutionsUniversity Health NetworkUniversity of Toronto
FundersNational Institute of Allergy and Infectious DiseasesCanadian Institutes of Health ResearchUniversity of TorontoBill and Melinda Gates Foundation
KeywordsPublic healthCommercializationContext (archaeology)Reproductive healthHealth policyDeveloping countryBiotechnologyEconomic growthGlobal healthMedicinePolitical sciencePublic relationsEnvironmental healthPopulationBiologyNursingEconomics

Abstract

fetched live from OpenAlex

BACKGROUND: Reproductive technologies have been extensively debated in the literature. As well, feminist economists, environmentalists, and agriculturalists have generated substantial debate and literature on gender. However, the implications for women of health biotechnologies have received relatively less attention. Surprisingly, while gender based frameworks have been proposed in the context of public health policy, practice, health research, and epidemiological research, we could identify no systematic framework for gender analysis of health biotechnology in the developing world. DISCUSSION: We propose sex and gender considerations at five critical stages of health biotechnology research and development: priority setting; technology design; clinical trials; commercialization, and health services delivery. SUMMARY: Applying a systematic sex and gender framework to five key process stages of health biotechnology research and development could be a first step towards unlocking the opportunities of this promising science for women in the developing world.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.718
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.127
GPT teacher head0.425
Teacher spread0.298 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations9
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueBMC International Health and Human RightsSame topicSex and Gender in HealthcareFrench-language works237,207