Pain mitigation during vaccine injections of the frail older adult population: A systematic review
Bibliographic record
Abstract
Background: Adults require disease-appropriate immunizations against a variety of vaccine preventable illnesses. The frail older adult population is at particular risk if not appropriately immunized due to increased vulnerability of morbidity and mortality. Despite this, adult immunization rates continue to be suboptimal, and studies have shown that pain mitigation is an important, modifiable factor in low immunization rates.Objectives: To determine the effective pain mitigating interventions available for use by the frail older adult population during routine vaccine injections.Methods: A systematic review of randomized controlled trials and quasi-randomized controlled trials was conducted, evaluating the effectiveness of available pain-relieving interventions during vaccine injections of the frail older adult population. Original articles were searched through MEDLINE via Ovid, EMBASE, the Cochrane Central Database, CINAHL via EBSCOhost, and grey literature until February 2017.Results: There were no documented trials to investigate the effectiveness of pain-mitigating strategies during vaccine injections of the frail older adult population.Conclusion: This systematic review demonstrates the need for investigation and further research of pain-mitigating strategies in the immunization of frail older adults. Research in this field may help to improve vaccination rates in this population.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".