Influence of Covid-19 Crisis Communication on Public Perception of the Kenyan Government Communication Strategy: A Case of Kibra Sub-County Nairobi, Kenya
Bibliographic record
Abstract
COVID-19 communication has drawn a sharp focus across the globe and elicited varied publicperceptions. This study sought to assess the influence of covid-19 crisis communication onpublic perception of the Kenyan government communication strategy with a case of Kibra subcounty Nairobi, Kenya, from March 2020 to December 2020. The main objective was to assesshow the government's daily speeches influenced the public interpretation of the COVID-19crisis. The rationale was based on assessing the public perception of the government’scommunication strategy. The findings of this study will be be helpful to communicationexperts and will help in improving the existing crisis communication strategies. This waslimited to Lindi ward in Kibra sub-county. Source Credibility Theory (CT) and SituationalCrisis Communication Theory (CCT) were used for understanding perception. This studyadopted a survey design to collect quantitative data involving 123 household heads sampledpurposively in Lindi, Kibra through a modified Crisis and Emergency Risk Communication(CERC) questionnaire. Averagely, 1% of the respondents disagreed with questions regardingCOVID-19 daily speeches, 8.5% indicated neutrality, and 90.5% agreed with the statements.The study concluded that the public's perceptions of government communication strategyregarding the COVID-19 crisis were favorable. The study recommends investigating theimpact of the centralization of the Kenyan government communication departments on crisiscommunication.Keywords: Crisis Communication, Public Perception, Communication Strategy
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".