Evidence map›Paper›PMID 33776208›Full record

ArticleNatural hazards (Dordrecht, Netherlands)2021

Reducing patient surge: community based social networks as first responders.

Alan Kirschenbaum

Open access · bronzeAbstract read
In one paragraph

Article in Natural hazards (Dordrecht, Netherlands), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 33% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author at 1 institution in 1 country.

Alan Kirschenbaum36586 Sde Yakov, Israel.
University of Skövde · SE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A major challenge for health services worldwide is in providing adequate medical care during mass disasters. The ongoing COVID-19 pandemic highlights this difficulty. Patient surge, a consequence of most types of disasters that contribute to trauma experiences, is a primary factor in disrupting such care as it is composed of worried well persons and those experiencing psychosocial trauma that can severely disrupt and overwhelm effective acute hospital based health care. We review the literature and propose a potential solution framework to reduce such a surge that relies on exploiting community social networks as first responders. We utilize and integrate literature based evidence on patient surge, community disaster behaviors and community based informal social networks to examine reasons for patient surge to hospitals. We then propose that leveraging community based social networks as a potent deterrent for non-critically injured, especially those who have experienced psychosocial trauma or the worried well, from seeking hospital care during ongoing disasters. By emphasizing the social capital inherent in community based social networks, this perspective posits an alternative cost-effective means of reducing patient surge.

Indexed as

DisastersPatient surgeSocial networksSurge modelWalking worried

Identifiers

PMID33776208
PMCPMC7985742
OpenAlexW3138363422

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.