Childhood immunization appointment reminders and recalls: strengths, weaknesses and opportunities to increase vaccine coverage
Bibliographic record
Abstract
OBJECTIVES: Childhood immunization coverage has been shown to be greatly impacted by parental forgetfulness regarding immunizations and appointments. Evidence supports the use of reminders and recalls to overcome this barrier, which remind parents about upcoming immunization appointments and inform them once their child is overdue for an immunization. In this study, we sought to identify reminder/recall strategies used throughout a large Canadian province and determine the perceived strengths, weaknesses and areas of improvement of existing strategies. STUDY DESIGN AND METHODS: An environmental scan was performed in 2018 in two phases: (1) interviews with public health leaders from the five zones of Alberta and (2) an online survey of public health centres across the province. Data analysis occurred in 2018 and 2019. RESULTS: Commonly reported strengths of reminders and recalls included their ability to increase appointment attendance and remind parents about immunizations, respectively. A major identified weakness was their time-consuming/resource-intensive nature. Many participants believed reminder/recalls could be improved by modernizing delivery methods. Educational information or strategies to overcome language barriers were rarely incorporated into reminder/recall systems. CONCLUSIONS: There was support for incorporating text messaging and automation into reminder/recall systems while encouraging continued exploration of novel reminder/recall delivery methods. Tailoring reminder/recalls to the needs and preferences of target populations can maximize the effectiveness of these systems. This includes modernizing methods of delivery, addressing language barriers, providing educational information, and allotting some degree of flexibility to local level management of reminder/recalls.
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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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".