Impact of the COVID-19 Pandemic on the Palestinian Family: A Community-Based Cross-Sectional Country-Level Online Survey (Preprint)
Bibliographic record
Abstract
UNSTRUCTURED It is imperative to take lessons from the current COVID-19 Pandemic situation and to ensure that governments and local institutions have the knowledge to improve their actions. The current community-based cross-sectional descriptive study aims to better understand and assess more fully the consequences that the present COVID-19 pandemic is having on the Palestinian family using a structured online questionnaire which was distributed through a social media platform (Facebook) between 29 April 2020 and 5 June 2020. A total of 570 adults aged 18 years or over participated in the study. The vast majority of the study participants 549 (96.3%) reported that water supplies were not always available in the home during the period of the COVID-19 pandemic. However, paying attention to personal hygiene and home cleaning was more than usual before the announcement of the COVID-19 pandemic. In general, following the onset of the pandemic, around three-fourths of the study participants, 417 (73.2%) reported that the containment measures of the COVID-19 pandemic have put an additional burden on their families. There was a clear limitation in people's movement after the announcement of the COVID-19 pandemic. We suggest discussing the obtained results in focus groups with local and national stakeholders ensuring in knowledge translation towards the community.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".