Participation in an action research project on vaccine services for children: relationship with vaccine delays
Bibliographic record
Abstract
Multicomponent interventions are effective in improving vaccine coverage. However, few studies have assessed their effect on timely vaccination. The aim of this study was to compare the proportion of children with vaccine delays at 2- and 12-month visits according to whether or not health centers have participated in an action research project on the organization of vaccination services for 0-5-year-olds. The action research project included a multicomponent intervention and was conducted between 2011 and 2015 in Quebec, Canada. An ecological before/after design was used for this analysis. A total of 264,579 DTaP-IPV-Hib (2-month visits) and 240,541 Men-C-C (12-month visits) vaccine doses were administered during 2011–2012 to 2014–2015 fiscal years, including 19% in 14 participating health centers and the remaining in 78 nonparticipating centers. Vaccine delays demonstrated a more pronounced decreasing trend in participating versus nonparticipating health centers (p < .0001 at 2 and 12 months). Between 2011–2012 and 2014–2015, participating centers managed to eliminate 35% of their vaccine delays at 2-month visits and 33% at 12-month visits, whereas nonparticipating centers eliminated 19% of delays at both visits. Our results are consistent with a positive impact of the multicomponent intervention, despite the fact that it had not specifically aimed at decreasing vaccine delays.
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 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".