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Record W4378781213 · doi:10.1080/17441692.2023.2216321

Not business as usual: Engaging the corporate sector in India’s TB elimination efforts

2023· review· en· W4378781213 on OpenAlexaboutno aff
Madeline Carwile, Chelsie Cintron, Komal Jain, Giancarlo Buonomo, Matt Oliver, Madolyn Dauphinais, Prakash Babu Narasimhan, Senbagavalli Prakash Babu, Sonali Sarkar, Lindsey M. Locks, Nalin Kulatilaka, Natasha S. Hochberg, Subitha Lakshminarayanan, Pranay Sinha

Bibliographic record

VenueGlobal Public Health · 2023
Typereview
Languageen
FieldMedicine
TopicTuberculosis Research and Epidemiology
Canadian institutionsnot available
FundersNational Institute of Allergy and Infectious DiseasesIndian Council of Medical ResearchOffice of AIDS ResearchWarren Alpert Foundation
KeywordsAbsenteeismIncentiveProductivityEconomic growthPrivate sectorTuberculosisDevelopment economicsBusinessQuarter (Canadian coin)EconomicsMedicineMarket economyManagement

Abstract

fetched live from OpenAlex

India has the highest global burden of tuberculosis (TB), accounting for a quarter of the worldwide TB disease incidence. Given the magnitude of India's epidemic, TB has enormous economic implications. Indeed, the majority of individuals with TB disease are in their prime years of economic productivity. Absenteeism and employee turnover due to TB have economic ramifications for employers. Furthermore, TB can easily spread in the workplace and compound the economic impact. Employers who fund workplace, community, or national TB initiatives stand to gain directly and also enjoy reputational benefits, which are important in the era of socially conscious investing. Corporate social responsibility laws in India and tax incentives can be leveraged to bring the logistical networks, reach, and innovative spirit of the private sector to bear on India's formidable TB epidemic. In this perspective piece, we explore the economic impacts of TB; opportunities for and benefits from businesses contributing to TB elimination efforts; and strategies to enlist India's corporate sector in the fight against TB.

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)0.009
metaresearch head score (Gemma)0.008
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.945
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.284
GPT teacher head0.462
Teacher spread0.177 · 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
GenreReview

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

Citations7
Published2023
Admission routes1
Has abstractyes

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