A literature review of the impact of pharmacy students in immunization initiatives
Bibliographic record
Abstract
BACKGROUND: Pharmacy students can help protect the public from vaccine-preventable diseases by participating in immunization initiatives, which currently exist in some Canadian and American jurisdictions. The objective of this article is to critically review evidence of student impact on public health through their participation in vaccination efforts. METHODS: PubMed, CINAHL, Cochrane Database, EMBASE, International Pharmaceutical Abstracts, Scopus and Web of Science electronic databases were searched for peer-reviewed literature on pharmacy student involvement in vaccination programs and their impact on public health. Papers were included up to November 17, 2015. Two reviewers independently screened titles and abstracts and extracted data from eligible full-text articles. RESULTS: Eighteen titles met all inclusion criteria. All studies were published between 2000 and 2015, with the majority conducted in the United States (n = 12). The number of vaccine doses administered by students in community-based clinics ranged from 109 to 15,000. Increases in vaccination rates in inpatient facilities ranged from 18.5% to 68%. Across studies, student-led educational interventions improved patient knowledge of vaccines and vaccine-preventable diseases. Patient satisfaction with student immunization services was consistently very high. DISCUSSION: Methodology varied considerably across studies. The literature suggests that pharmacy students can improve public health by 1) increasing the number of vaccine doses administered, 2) increasing vaccination rates, 3) increasing capacity of existing vaccination efforts, 4) providing education about vaccines and vaccine-preventable diseases and 5) providing positive immunization experiences. CONCLUSION: Opportunities exist across Canada to increase pharmacy student involvement in immunization efforts and to assess the impact of their participation. Greater student involvement in immunization initiatives could boost immunization rates and help protect Canadians from vaccine-preventable diseases.
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 | 0.013 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.029 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".