From the COVID-19 Pandemic to the Mental Health of the Philippines: Modelling the Cascade of Disasters using an Influence Diagram
Bibliographic record
Abstract
Abstract The COVID-19 pandemic had not only physical, but also mental effects due to the anxiety of being infected, necessity of community quarantine, and the big shift in lifestyle over the course of the pandemic. In this study, a Bayesian network was constructed and informed using the responses of 1,605 Filipinos to a survey conducted online during August - September 2021. The main objective of this study is to identify the critical factors that caused the cascade of disasters from the COVID-19 pandemic to a lower state of well-being for the community, by observing how it affected the mental health of the Philippine citizens. To achieve this goal, a Bayesian network was used to assess the state of the community, which was then extended into an influence diagram. Along with expert opinion on possible interventions (decision nodes), we can obtain an optimal set of interventions/decision nodes in order to lift the community’s state of mental well-being. The study aims to identify the proper decisions to make if another pandemic were to occur as well as become a framework for future applications of the Bayesian network into practical fields.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".