Addressing vaccine hesitancy and access barriers to achieve persistent progress in Israel’s COVID-19 vaccination program
Bibliographic record
Abstract
As of March 31, 2021, Israel had administered 116 doses of vaccine for COVID-19 per 100 population (of any age) – far more than any other OECD country. It was also ahead of other OECD countries in terms of the share of the population that had received at least one vaccination (61%) and the share that had been fully vaccinated (55%). Among Israelis aged 16 and over, the comparable figures were 81 and 74%, respectively. In light of this, the objectives of this article are: 1. To describe and analyze the vaccination uptake through the end of March 2021 2. To identify behavioral and other barriers that likely affected desire or ability to be vaccinated 3. To describe the efforts undertaken to overcome those barriers Israel’s vaccination campaign was launched on December 20, and within 2.5 weeks, 20% of Israelis had received their first dose. Afterwards, the pace slowed. It took an additional 4 weeks to increase from 20 to 40% and yet another 6 weeks to increase from 40 to 60%. Initially, uptake was low among young adults, and two religious/cultural minority groups - ultra-Orthodox Jews and Israeli Arabs, but their uptake increased markedly over time. In the first quarter of 2021, Israel had to enhance access to the vaccine, address a moderate amount of vaccine hesitancy in its general population, and also address more intense pockets of vaccine hesitancy among young adults and religious/cultural minority groups. A continued high rate of infection during the months of February and March, despite broad vaccination coverage at the time, created confusion about vaccine effectiveness, which in turn contributed to vaccine hesitancy. Among Israeli Arabs, some residents of smaller villages encountered difficulties in reaching vaccination sites, and that also slowed the rate of vaccination. The challenges were addressed via a mix of messaging, incentives, extensions to the initial vaccine delivery system, and other measures. Many of the measures addressed the general population, while others were targeted at subgroups with below-average vaccination rates. Once the early adopters had been vaccinated, it took hard, creative work to increase population coverage from 40 to 60% and beyond. Significantly, some of the capacities and strategies that helped Israel address vaccine hesitancy and geographic access barriers are different from those that enabled it to procure, distribute and administer the vaccines. Some of these strategies are likely to be relevant to other countries as they progress from the challenges of securing an adequate vaccine supply and streamlining distribution to the challenge of encouraging vaccine uptake.
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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 | 0.003 | 0.005 |
| 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".