What Made the COVID-19 Pandemic Experience Worse in Communities in Northern Nigeria: Fuzzy Cognitive Mapping of Community Perceptions
Bibliographic record
Abstract
Aims Collate local perceptions of factors influencing experience of the COVID-19 pandemic in communities in Bauchi State, Northern Nigeria. Results Fuzzy cognitive mapping (FCM) collated participant views of what made their experience worse during the COVID-19 pandemic. FCM uses concepts linked by weighted arrows to indicate perceived causal relationships. Higher weights indicate stronger influences; positive and negative signs indicate direct and inverse causal relationships, respectively. In late 2023, local facilitators collected 81 maps in urban, rural and remote communities, 11 with administrative officials, and four with vulnerable groups (388 participants in total). We created average maps for each stakeholder group. Facilitators inductively grouped factors into categories. We calculated the cumulative net influence (CNI) (range −1 to +1) of categories and identified important causes and outcomes within the network. The maps included 152 factors in 25 categories. Hunger and lack of food (CNI = 0.63) worsened pandemic experience the most, followed by reduced businesses and jobs (CNI = 0.40), causing economic disruption and threatening livelihoods. Increased household conflicts (CNI = 0.35) and stress and mental health problems (CNI = 0.30) were also prominent negative influences and intermediate outcomes in the network. Lockdown (CNI = 0.34) was the most important underlying cause of other causal categories. Conclusions The maps depicted the interconnected impacts of the pandemic on community members. Participants confirmed the worst impacts were related to control measures exacerbating pre-existing economic challenges. These FCM findings will form part of the evidence shared with communities and policy makers to support co-design of strategies for pandemic recovery aligned with community needs and strengths.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".