The long and winding road to improving immunization rates: Sharing best practices in Canada
Bibliographic record
Abstract
Vaccines are one of the most effective ways to decrease childhood mortality. Unfortunately, however, Canada placed 28th out of 29 high-income countries in a 2013 UNICEF report that compared national uptake rates of early childhood immunizations. Work is underway to address this issue as reflected in the 2016 federal budget which highlights the importance of improving access to immunization. There are many steps that can be taken to improve vaccine uptake, such as identifying and better understanding the individual and program level factors that underlie delay or refusal to receive vaccines. However, it is challenging to find evidence and ensure its relevancy within the Canadian context. Targeted resources are needed that address the complexity of immunization along the entire continuum from vaccine manufacture through to patient uptake. Although there is a lot of information relevant to Canada, it has not been gathered together in one "go to" site and it is not curated. Canada needs a solid, easily accessible, user-friendly platform for sharing what works in immunization with health care professionals as well as parents and patients. This platform would be a major step in facilitating vaccine acceptance in Canada.
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 | 0.014 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.007 | 0.002 |
| Research integrity | 0.017 | 0.027 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".