Responding to the covid-19 in West Bank Palestine refugee camps: lessons and role of community engagement
Bibliographic record
Abstract
BACKGROUND: Global unpreparedness was noted, where even high-income countries with their established healthcare systems could not cope with the Covid-19 pandemic. In the Occupied Palestinian Territory (OPT), especially in the Palestinian refugee camps, Covid-19 was an additional burden on multiple levels. OBJECTIVE: The aim of this study is to understand the notion of Covid-19 responses in the West Bank refugee camps and the health system's ability to meet the needs of the refugees as well as the role of local community actors in the response. METHODS: Qualitative data were collected through semi-structured interviews. In total, 27 interviews were conducted with popular committees in camps, professionals working at the Palestinian Ministry of Health in addition to local and international health-related non-governmental organizations (NGOs). Participants were contacted via phone calls, Zoom meetings and in-person, for one to one and a half hours maximum. Questions were about the impact of Covid-19 and the way the participants and their organizations responded to this pandemic. RESULTS: Our findings state that wide-scale multilevel Covid-19 responses were conducted from different committees and institutions in the OPT. For example, the popular committees took part in distributing medicines, food parcels and hygiene kits, and the NGOs provided refugees with educational materials and psychosocial support. However, the overstretched Palestinian health system, the limited resources in addition to the poor coordination between health providers and poor follow up of the imposed restrictions, hindered the fast and effective response. Community engagement was a remarkable element which contributed to the successful deployment of response plans. This was demonstrated by the collaboration of the camps' local bodies in addition to the initiatives of the local community in camps. CONCLUSION: Covid-19 impacts were particularly pronounced for refugees where response efforts did not fulfil their needs. The study highlights the importance of preparedness, working with community organisations and designing interventions in a human-centred/community-centred way to increase the effectiveness of health interventions and responses.
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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 | 0.022 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.024 | 0.022 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.004 | 0.028 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.009 | 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".