Question · asked by 1 review
Educational intervention with information and practical components (theoretical oral health education plus practical skills training) for nursing staff and/or residents vs (Optimised) usual care: Oral health-related attitude of nursing staff
- Population
- Nursing home nursing staff (approximately 95% female in the two contributing studies)
- Intervention
- Educational intervention with information and practical components (theoretical oral health education plus practical skills training) for nursing staff and/or residents
- Comparison
- (Optimised) usual care
- Outcome
- Oral health-related attitude of nursing staff
Educational intervention with information and practical components… showed no clear difference in oral health-related attitude of nursing staff compared with (Optimised) usual care; GRADE not reported.
What each review found
Current versions only| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Educational intervention with information and practical components (theoretical oral health education plus practical skills training) for nursing staff and/or residents vs (Optimised) usual care Oral health-related attitude of nursing staff · Nursing home nursing staff (approximately 95% female in the two contributing studies) Educational intervention with information and practical components… showed no clear difference in oral health-related attitude of nursing staff compared with (Optimised) usual care; GRADE not reported. Oral health educational interventions for nursing home staff and residents (2016) · Cochrane All outcomes | SMD 0.30 [-0.23, 0.83] | 2 studies · 459 participants | GRADE not reported |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Oral health educational interventions for nursing home staff and residents 2016 · Cochrane All outcomes | 2016-01 | Not listed | Not assessed |
How reviews are matched to this question
Reviews word the same question differently. Four decision models (TypeSafe's Jev, Liquid AI's d1-ultra, SubQ Snap and Baseten's Elephant) judged each wording of the population, intervention, comparator and outcome against the others; two wordings count as the same when at least three of them agree and none clearly disagrees, and findings count as this question only when all four elements match. Timing, setting and subgroup never split a question: they are shown on each finding.