Bibliographic record
Abstract
Vaccine-preventable diseases (VPD) are responsible for a significant portion of mortality across the life course in both low-income countries and in medium- and high-income countries. Yet, countries are consistently below the adult influenza vaccination targets, with rates in recent times even falling in some areas. (1) The study Towards Ending Immunization Inequity seeks to understand the various factors that contribute to the accessibility and effectiveness of vaccine-related messages and campaigns including the effects of social determinants, with the knowledge that these opportunities for communication represent a unique policy lever to improving uptake rates of vaccination in the most at-risk communities. (2) To address this knowledge gap, a 3-phase mixed-methods study was conducted including a preliminary scan of existing vaccine schedules and NITAG recommendations, focus groups and a cross-sectional survey. (3) Study results indicated that social determinants play a key role in an individual's knowledge of vaccine-related information including types of vaccines available, vaccination gateways, vaccine recommendations and vaccine safety. (4) However, knowing that social determinants can influence uptake rates does not readily create opportunities and entry points for governments to implement tangible actions. An accessible entry point to reducing and ending immunization inequity is through changes in public health messaging to reach those who are currently unreachable.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.020 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.001 | 0.012 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".