Psychological Impact of the COVID-19 Pandemic and Social Determinants on the Portuguese Population: Protocol for a Web-Based Cross-sectional Study
Bibliographic record
Abstract
BACKGROUND: The COVID-19 outbreak and consequent physical distance measures implemented worldwide have caused significant stress, anxiety, and mental health implications among the general population. Unemployment, working from home, and day-to-day changes may lead to a greater risk of poor mental health outcomes. OBJECTIVE: This paper describes the protocol for a web-based cross-sectional study that aims to address the impact of the COVID-19 pandemic on mental health. METHODS: Individuals from the general population aged 18 years or more and living in Portugal were included in this study. Data collection took place between November 10, 2020, and February 10, 2021. An exponential, nondiscriminative, snowball sampling method was applied to recruit participants. A web-based survey was developed and shared on social media platforms (eg, Facebook, Instagram, Twitter, LinkedIn, and WhatsApp groups) and through e-mail lists for recruitment of the seeds. RESULTS: Data analysis will be performed in accordance with the different variables and outcomes of interest by using quantitative methods, qualitative methods, or mixed methods, as applicable. A total of 929 individuals had completed the web-based survey during the 3-month period; thus, our final sample comprised 929 participants. Results of the survey will be disseminated in national and international scientific journals in 2021-2022. CONCLUSIONS: We believe that the findings of this study will have broad implications for understanding the psychological impact of the COVID-19 pandemic on Portuguese residents, as well as aspects related to the informal economy. We also hope that the findings of this study are able to provide insights and guidelines for the Portuguese government to implement action. Finally, we expect this protocol to provide a roadmap for other countries and researchers that would like to implement a similar questionnaire considering the related conclusions. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/28071.
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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 | 0.023 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.038 | 0.008 |
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".