Memahami Faktor-Faktor yang Memengaruhi Tingkat Vaksinasi Covid-19: Menggali Peranan Determinan Sosial di Ternate
Bibliographic record
Abstract
Latar Belakang: Survei penerimaan vaksin COVID-19 di Indonesia belum menyebutkan secara spesifik prevalensinya di Maluku Utara, khususnya di Kota Ternate. Salah satu media lokal menulis bahwa pejabat daerah dan petugas kesehatan masih ragu dengan keamanan vaksin tersebut. Penelitian ini bertujuan untuk mengetahui determinan sosial yang berhubungan dengan penerimaan vaksin COVID-19 pada masyarakat Kota Ternate pada tahun 2021Metode: Penelitian Cross-sectional dilakukan di Kota Ternate pada bulan April sampai Mei 2021 dengan jumlah sampel sebanyak 178 orang yang dipilih menggunakan multistage cluster sampling. Sampelnya adalah warga yang tinggal di Kota Ternate dan berusia lebih dari 18 tahun, sedangkan kriteria eksklusi adalah menolak peserta wawancara langsung. Data primer dikumpulkan menggunakan kuesioner semi-terstruktur untuk mengumpulkan data demografi, pengetahuan, dan penerimaan vaksin. Analisis dilakukan menggunakan regresi logistik untuk menilai faktor yang paling berpengaruh terhadap penerimaan vaksin.Hasil: Lebih dari separuh responden menolak vaksin COVID-19 (59,40%), dengan alasan paling umum adalah tidak yakin akan efektivitasnya (42,60%) dan tidak yakin akan keamanannya (36,60%). Status perkawinan (PR=0,23; 95% CI 0,08-0,62), pendapatan (PR=4,45; 95% CI 1,86-10,58), riwayat infeksi COVID-19 (PR=0,20; 95% CI 0,08-0,45), dan pengetahuan (PR=8,97; 95% CI 3,77-21,27) berpengaruh terhadap penerimaan vaksin COVID-19 dengan p<0,05.Kesimpulan: Status perkawinan, pendapatan, riwayat terinfeksi COVID-19, dan pengetahuan ditemukan sebagai faktor yang berhubungan dengan penerimaan vaksin COVID-19. Disarankan adanya pendekatan untuk mengatasi penolakan vaksinasi, seperti memperkuat media layanan kesehatan untuk memberikan informasi yang dibutuhkan dan mengkampanyekan vaksin COVID-19 melalui media sosial, serta tatap muka.Kata Kunci: COVID-19, Determinan sosial, Penerimaan vaksin Background: The survey of COVID-19 vaccine acceptance in Indonesia has not specifically stated the prevalence in North Maluku, especially in Ternate City. One local media wrote that regional officials and health workers were still unsure of the vaccine's safety. This study aimed to determine related determinant factors of COVID-19 vaccine acceptance among people in Ternate in 2021.Methods: A Cross-sectional study was conducted in Ternate from April to May 2021, with 178 samples selected using multistage cluster sampling. The sample were residents who lived in Ternate and were more than 18 years old, while the exclusion criteria were refused to direct interview participants. Primary data were collected using questionnaires. A semi-structured questionnaire collected demographics, knowledge, and vaccine acceptance. Data were analyzed using logistic regression to assess the most influential factors on vaccine acceptance.Result: More than half of the respondents refused the COVID-19 vaccine (59.40%), with the most common reasons being unsure of its effectiveness (42.60%) and unsure of its safety (36.60%). Marital status ((PR=0,23; 95% CI 0,08-0,62), income (PR=4.45; 95% CI 1.86-10.58), history of COVID-19 infection (PR=0.20; 95% CI 0.08-0.45), and knowledge (PR=8.97; 95% CI 3.77-21.27) affected the acceptance of COVID-19 vaccine with p<0.05.Conclusion: Marital status, income, history of being infected with COVID-19, and knowledge were found as factors related to COVID-19 vaccine acceptance. It is recommended that there be an approach to overcome vaccination refusal, such as strengthening health service media to provide the information needed and campaigning the COVID-19 vaccine through social media. as well as face-to-face.Keywords: COVID-19, Social determinant, Vaccine acceptance
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".