Exploring the Perceptions of Nurses on Receiving the SARS CoV-2 Vaccine in Palestine: A Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Uncertainty about vaccination among nurses are major barriers to managing the ongoing COVID-19 pandemic worldwide. PURPOSE: To explore nurseś perceptions about receiving the SARS CoV-2 vaccine to inform the upcoming Palestinian Ministry of Health (MOH) vaccination efforts. METHODS: Four focus groups were conducted with nurses between January 18 and 30, 2021, before MOH launched vaccinations in Palestine. Participants working in government and private facilities were invited to participate and completed an online or paper form to provide demographics, review the study purpose, and give consent. Meetings were facilitated in Arabic either online via the Zoom platform or face-to-face using the same interview guide. Transcripts were translated into English and coded using a template analysis approach. RESULTS: Forty-six nurses, with a median age of 29.5y (range, 22-57) from across Palestine participated. Three major themes emerged: uncertainty, trust, and the knowledge needed to move forward. Uncertainty related to the evolving nature of COVID-19, the rapidity of vaccine development, the types and timing of available vaccines. The need for trusted experts to share scientific information about the vaccines to counteract the misinformation in social media. Moreover, reliable vaccine information may help vaccine-hesitant nurses move to vaccine-acceptors and to convince others, including their patients. CONCLUSION: The negative perception of nurses towards vaccines is problematic in Palestine and uncertainty about which vaccine(s) will be available adds to the lack of education and mass-media misinformation. Other countries with vaccination efforts that are not wholly planned or implemented and may be struggling with similar concerns
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".