The Mental Health Impacts of a Pandemic: A Multiaxial Conceptual Model for COVID-19
Bibliographic record
Abstract
The COVID-19 pandemic substantially impacted the mental health of the general population and particularly vulnerable individuals and groups. A wealth of research allows for estimating this impact and identifying relevant factors contributing to or mitigating it. The current paper presents and synthesizes this evidence into a multiaxial model of COVID-19 mental health impacts. Based on existing research, we propose four axes: (1) Exposure to COVID-related events; (2) Personal and social vulnerability, such as previous mental health problems or belonging to a vulnerable group; (3) Time, which accounts for the differential impacts throughout the development of the pandemic; and (4) Context, including healthcare and public policies, and social representations of the illness influencing individual emotional reactions and relevant behaviors. These axes help acknowledge the complexity of communities' reactions and are pragmatic in identifying and prioritizing factors. The axes can provide individual information (i.e., more exposure is harmful) and account for interactions (e.g., exposure in an early phase of the pandemic differs from a later stage). This model contributes to the reflections of the evidence and informs the mental health response to the next pandemic.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".