Healthcare workers' current practices, knowledge/awareness, barriers, and attitudes/perceptions related to pneumococcal vaccination of older adults: A mixed-methods systematic review
Bibliographic record
Abstract
OBJECTIVES: Despite global efforts, pneumococcal vaccination uptake among older adults remains significantly low. Previous literature suggests that vaccine promotion by healthcare workers plays an essential role in uptake. We conducted a systematic review to examine healthcare workers' current practices, and to identify capability, opportunity, and motivation factors regarding pneumococcal vaccination for older adults. METHODS: A search of both quantitative and qualitative studies was conducted in MEDLINE, Embase, CINAHL, Global Health, AgeLine, and Scopus databases and in the grey literature to identify relevant published studies from inception to November 2, 2023, with no language restrictions. The Joanna-Briggs Institute methodology for mixed-methods systematic reviews was used (PROSPERO ID: CRD42023480576). Studies conducted on healthcare workers related to pneumococcal vaccination in older adults (65 years and older) were included. The quality of studies was assessed using the Mixed Methods Appraisal Tool for the quantitative and mixed-methods studies and the Critical Appraisal Skills Programme Tool for qualitative studies. In addition, we used the convergent integrated and segregated approach to synthesize proportions and relevant themes. RESULTS: We included 42 studies (38 quantitative and four qualitative), of which 20 evaluated factors related to current practice, 27 capability, 24 opportunity, and 28 motivations. Our review findings suggest that practices varied considerably. However, most health providers knew about pneumococcal vaccination and had a positive attitude/perception toward it. Common barriers to pneumococcal vaccination in older adults included finances (e.g., high cost to patients/system), logistics (e.g., insufficient time/information), availability of information (e.g., insufficient education/campaigns), patient-related (e.g., hesitancy/refusal), and system factors (e.g., unclear guidelines/recommendations). CONCLUSIONS: As the aging population rises, it is imperative to prioritize global efforts to protect older adults from vaccine-preventable illnesses, such as pneumococcal disease. The current study identifies critical barriers to address among healthcare workers to improve vaccination rates among older adults.
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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 | 0.025 | 0.087 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.010 |
| Bibliometrics | 0.014 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".