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Record W3083425177 · doi:10.3329/bioethics.v9i3.48912

Mass Vaccination Programme: Public Health Success and Ethical Issues – Bangladesh Perspective

2020· article· en· W3083425177 on OpenAlexaff
Abu Sadat Mohammad Nurunnabi, Miliva Mozaffor, Mohammad Akram Hossain, Sadia Akther Sony

Bibliographic record

VenueBangladesh Journal of Bioethics · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsVaccinationMeaslesSmallpoxPublic healthContext (archaeology)PoliomyelitisMedicineDiphtheriaEnvironmental healthHerd immunityPolitical scienceEconomic growthImmunologyGeographyPediatricsEconomics

Abstract

fetched live from OpenAlex

Vaccines are responsible for many global public health successes, such as the eradication of smallpox and significant reductions in other serious infections like diphtheria, pertussis, tetanus, polio and measles. However, mass vaccination has also been the subject of various ethical controversies for decades. Several factors need to be considered before any vaccine is deployed at national programme like the potential burden of disease in the country or region, the duration of the protection conferred, herd immunity in addition to individual protection, vaccine-related risks, financing and the logistical feasibility of the large-scale vaccination. Moreover, several ethical dilemmas revolve around authority and mandates for vaccination, informed consent, benefits vs. risks, and disparities in access to vaccination. This review paper aims to elaborate the ethical issues involved in mass vaccination programme and present some additional challenges in the context of a resource-poor settings of public health in Bangladesh.

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)0.010
metaresearch head score (Gemma)0.016
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.010
Threshold uncertainty score0.052

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0040.003
Open science0.0010.002
Research integrity0.0040.004
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 head0.122
GPT teacher head0.399
Teacher spread0.277 · 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
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

Citations7
Published2020
Admission routes1
Has abstractyes

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