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Record W3179890351 · doi:10.1002/jcop.22652

Vaccination‐hesitancy and vaccination‐inequality as challenges in Pakistan's COVID‐19 response

2021· article· en· W3179890351 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Community Psychology · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsConcordia University
Fundersnot available
KeywordsVaccinationInequalityCoronavirus disease 2019 (COVID-19)Mass vaccinationDistribution (mathematics)Political scienceDiseaseMedicineImmunologyInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

This study explores the mechanism for timely and equitable distribution of coronavirus disease 2019 (COVID-19) vaccination among the various communities in Pakistan. It examines the factors that support and/or impede peoples' access and response towards COVID-19 vaccination in Pakistan. The study uses a literature synthesis approach to examine and analyze the situation of the COVID-19 vaccination in Pakistan. The research results show "hesitancy" and "inequality" as two fundamental challenges that hinder the successful delivery of COVID-19 vaccination in Pakistan. People are reluctant to use vaccines due to conspiracy theories and religious beliefs. However, inequality, especially unequal accessibility to all social groups appears to be a more significant barrier to getting a vaccine. We argue that there is a need to mobilize community influence, social media, and mass media campaigns for public education on vaccination programs along with the engagement of religious leaders to endorse the vaccination for the masses. The area of this study is underdeveloped; thereby, future studies are recommended to investigate the possible way for equitable distribution of vaccines in multiple regions.

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.

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)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.642
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.138
GPT teacher head0.472
Teacher spread0.333 · 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