Characteristics of immunisation support programmes in Canada: a scoping review and environmental scan
Bibliographic record
Abstract
OBJECTIVE: To identify, characterise and map the existing knowledge about programmes that provide immunisation support to Canadians and barriers and facilitators to their delivery. DESIGN: Scoping review and environmental scan. INTRODUCTION: Vaccine hesitancy may be associated with unmet support needs of individuals. Immunisation support programmes that provide multicomponent approaches can improve vaccine confidence and equitable access. INCLUSION CRITERIA: Canadian programmes that focus on providing information about immunisation for the general public, but excluding articles targeting health professionals. The primary concept involves mapping the characteristics of programmes and our secondary concept examines barriers and facilitators to programme delivery. METHODS: The Joanna Briggs Institute (JBI) methodology guided this review, reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews. A search strategy was developed and translated for six databases in November 2021 (updated October 2022). Unpublished literature was identified through the Canadian Agency for Drugs and Technologies in Health Grey Matters checklist and other relevant sources. Stakeholders (n=124) from Canadian regional health authorities were also contacted by email for publicly accessible information. Two independent raters screened and extracted data from identified material. Results are presented in tabular form. RESULTS: The search strategy and environmental scan resulted in 15 287 sources. A total of 161 full-text sources were reviewed after applying eligibility criteria, resulting in 50 articles. Programmes were delivered in multiple Canadian provinces, focusing on various vaccine types. All programmes aimed to increase vaccine uptake and were mostly provided in person. Multidisciplinary delivery teams based on collaborations among multiple entities were credited as a facilitator to programme delivery across settings. Limitations on programme resources, attitudes of programme staff and participants, and systems organisation were identified as barriers to delivery. CONCLUSIONS: This review highlighted characteristics of immunisation support programmes across various settings and described multiple facilitators and barriers. These findings can inform future interventions that aim to support Canadians in making decisions about immunisation.
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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 | 0.034 | 0.147 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.056 | 0.100 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".