Attitudes and Satisfaction toward the Taken Procedures to Tackle COVID-19 Pandemic in Palestine
Bibliographic record
Abstract
BACKGROUND: Since the beginning of the COVID-19 pandemic, there have been differences in the mitigation strategies implemented by governments worldwide. In addition, people's acceptance and adherence to these strategies, such as avoiding large gatherings and shelter in place, varied. The current study aims to assess the attitude and satisfaction with the procedures to tackle COVID-19 in Palestine. METHODS: This cross-sectional descriptive study was conducted in the Palestinian territories, including, Gaza Strip, West Bank, and East Jerusalem, between April 29, 2020, and June 5, 2020, using a validated online questionnaire. The questionnaire included three sections: socio-demographic characteristics, attitude towards the measures and behaviors to avoid COVID-19 infection and its consequences, and level of people satisfaction with the response of the community and local authorities to combat the COVID-19 pandemic. A convenience sampling method was used to select participants. Statistical analysis was performed using SPSS version 26. RESULTS: A total of 570 adults aged ≥18 years (56.3% males and 43.7% females) were included in the study. The mean positive attitude score (average % agree or strongly agree) was 94.22%; 95.24%, 95.18%, and 92.18% in the Gaza Strip, West Bank, and East Jerusalem, respectively. While, the mean satisfaction score was 44.26%, distributed as 47.16%, 46.1%, and 39.22% in the Gaza Strip, West Bank, and East Jerusalem, respectively. Additionally, there were statistically significant variations by most attitude and satisfaction variables across the governorates included in the study (p < 0.05). The current study demonstrated high levels of positive attitude but suboptimal level of satisfaction toward the taken procedures to tackle COVID-19 in Palestine. CONCLUSIONS: Varied implementation strategies to improve the levels of satisfaction toward the approaches to combat the COVID-19 pandemic are recommended.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".