Barriers to the use of reminder/recall interventions for immunizations: a systematic review
Bibliographic record
Abstract
BACKGROUND: Although many studies have demonstrated the benefits of reminder/recall (RR) measures to address patient under-immunization and improve immunization coverage, they are not widely implemented by healthcare providers. We identified providers' perceived barriers to their use from existing literature. METHODS: We conducted a systematic review of relevant articles published in English between January 1990 and July 2011 that examined the perceptions of healthcare providers regarding barriers to tracking patient immunization history and implementing RR interventions. We searched MEDLINE, PubMed, EMBASE, Cumulative Index to Nursing and Allied Health Literature, Academic Search Premier, and PsychINFO. Additional strategies included hand-searching the references of pertinent articles and related reviews, and searching keywords in Google Scholar and Google. RESULTS: Ten articles were included; all described populations in the United States, and examined perceptions of family physicians, pediatricians, and other immunization staff. All articles were of moderate-high methodological quality; the majority (n=7) employed survey methodology. The most frequently described barriers involved the perceived human and financial resources associated with implementing an RR intervention, as well as low confidence in the accuracy of patient immunization records, given the lack of data sharing between multiple immunization providers. Changes to staff workflow, lack of appropriate electronic patient-tracking functionalities, and uncertainty regarding the success of RR interventions were also viewed as barriers to their adoption. CONCLUSIONS: Although transitioning to electronic immunization records and registries should facilitate the implementation of RR interventions, numerous perceived barriers must still be overcome before the full benefits of these methods can be realized.
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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 | 0.037 | 0.156 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.010 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".