Primary objective: mobile stock notification with effective product transport vs usual care: Proportion of healthcare workers who reported a stockout of drugs in the last 30 days (stockout of artemether‐lumefantrine 2 X 6 to treat malaria caused by Plasmodium facilparum)
It is very uncertain whether primary objective: mobile stock notification with effective product… changes proportion of healthcare workers who reported a stockout of drugs in… compared with standard care (very low certainty).
What each review found
Include earlier review versions| Finding | Effect | Studies | GRADE certainty |
|---|---|---|---|
Primary objective: mobile stock notification with effective product transport vs standard care Proportion of healthcare workers who reported a stockout of drugs in the last 30 days (stockout of artemether‐lumefantrine 2 X 6 to treat malaria caused by Plasmodium facilparum) · healthcare workers and other health professionals involved in commodity and stock management It is very uncertain whether primary objective: mobile stock notification with effective product… changes proportion of healthcare workers who reported a stockout of drugs in… compared with standard care (very low certainty). Tracking health commodity inventory and notifying stock levels via mobile devices: a mixed methods… (2020) · Cochrane Headline outcomes only | RR 1.43 [0.81, 2.5] | 168 participants | Very low |
Findings are shown side by side, never pooled across reviews.
The reviews
| Review | Searched up to | Included studies | AMSTAR 2 |
|---|---|---|---|
| Tracking health commodity inventory and notifying stock levels via mobile devices: a mixed methods systematic review 2020 · Cochrane Headline outcomes only | Not reported | list not extracted yet | Not assessed |
Studies behind the findings
None of these reviews ties its included studies to this outcome in the data here. Cochrane reviews and reviews with headline outcomes only do not list their studies yet.
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.