For a structured response to the psychosocial consequences of the restrictive measures imposed by the global COVID-19 health pandemic: The MAVIPAN longitudinal prospective cohort study protocol
Bibliographic record
Abstract
ABSTRACT Background The COVID-19 pandemic and the isolation measures taken to control it has caused important disruptions in economies and labour markets, changed the way we work and socialize, forced schools to close and healthcare and social services to reorganize in order to redirect resources on the pandemic response. This unprecedented crisis forces individuals to make considerable efforts to adapt and can have serious psychological and social consequences that are likely to persist once the pandemic has been contained and restrictive measures lifted. These impacts will be significant for vulnerable individuals and will most likely exacerbate existing social and gender health and social inequalities. This crisis also puts a toll on the capacity of our healthcare and social services structures to provide timely and adequate care. In order to minimize these consequences, there is an urgent need for high-quality, real-time information on the psychosocial impacts of the pandemic. The MAVIPAN ( Ma vie et la pandémie/My life with the pandemic ) study aims to document how individuals, families, healthcare workers, and health organisations that provide services are affected by the pandemic and how they adapt. Methods The MAVIPAN study is a 5-year longitudinal prospective cohort study that was launched on April 29 th , 2020 in the province of Quebec which, at that time, was the epicenter of the pandemic in Canada. Quantitative data is collected through online questionnaires approximately 5 times a year depending on the pandemic evolution. Questionnaires include measures of health, social, behavioral and individual determinants as well as psychosocial impacts. Qualitative data will be collected with individual and group interviews that seek to deepen our understanding of coping strategies. Discussion The MAVIPAN study will support the healthcare and social services system response by providing the evidence base needed to identify those who are most affected by the pandemic and by guiding public health authorities’ decision making regarding intervention and resource allocation to mitigate these impacts. It is also a unique opportunity to advance our knowledge on coping mechanisms and adjustment strategies. Trial registration NCT04575571 (retrospectively registered)
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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 | 0.031 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.077 | 0.028 |
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".