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Record W4386785868 · doi:10.1177/09720634231195214

When Media Campaigns Fail: Explaining the Factors of Civil Disobedience to COVID-19 Protocols in Nigeria

2023· article· en· W4386785868 on OpenAlexaff
Obasanjo Joseph Oyedele, Toyin Segun Onayinka, Omolola Oluwasola, Chika Euphemia Asogwa

Bibliographic record

VenueJournal of Health Management · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsMemorial University of Newfoundland
Fundersnot available
KeywordsCivil disobedienceConstruct (python library)Government (linguistics)Social mediaCoronavirus disease 2019 (COVID-19)Civil societySocial psychologyPublic relationsPsychologyPolitical scienceLawMedicineComputer sciencePolitics

Abstract

fetched live from OpenAlex

Media campaigns on COVID-19 protocols were launched in Nigeria to reduce the spread of the virus. There was evidence to suggest disobedience to the protocols. Thus, this study investigates factors that facilitated civil disobedience to COVID-19 protocols and the implications for the management of the virus. Awareness, mediatisation and personal, cultural and societal factors constructs were formulated to measure the level of civil disobedience. Kaiser–Meyer–Olkin’s Measure of Sampling Adequacy used for the study revealed that 86.4% out of the sampled size was sufficient for testing and validating variables in the civil disobedience construct, 69.1% and 63.1% for awareness and mediatisation constructs, respectively. The constructs were subjected to inferential statistical analysis, and the variables measured at the continuous and linear relationship levels. The study found economic status and media reports as the determinants of peoples’ awareness of the virus. Provision of adequate information and exposure to COVID-related contents were dominant factors under mediatisation; social distancing and use of face masks were dominant factors of civil disobedience. This study concluded that media campaigns on COVID-19 protocols failed to achieve needed behavioural changes due to economic issues, language barriers, insensitivity of government and inadequate provision of essential amenities.

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)0.007
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.303
Threshold uncertainty score0.298

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.144
GPT teacher head0.441
Teacher spread0.296 · 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 designQualitative
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

Citations1
Published2023
Admission routes1
Has abstractyes

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