Evidence map›Paper›PMID 31367448›Full record

ArticleNursing open2019

Nurse staffing models in acute care: A descriptive study.

Marianne Thériault, Carl-Ardy Dubois, Roxane Borgès da Silva, Alexandre Prud'homme

Open access · goldAbstract read
In one paragraph

Article in Nursing open, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed, 1 pooled it
2.3field-weighted citation impact, top 11% 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

3 citing papers in PubMed, 1 synthesis or guideline pooled it, 12 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
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

4 authors at 1 institution in 1 country.

Marianne ThériaultFaculty of Nursing University of Montreal Montreal Quebec Canada.
Carl-Ardy DuboisPublic Health Research Institute University of Montreal Montreal Quebec Canada.ORCID https://orcid.org/0000-0001-7663-4796
Roxane Borgès da SilvaPublic Health Research Institute University of Montreal Montreal Quebec Canada.ORCID https://orcid.org/0000-0003-3614-8149
Alexandre Prud'hommePublic Health Research Institute University of Montreal Montreal Quebec Canada.
Montreal Clinical Research Institute · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

aimsTo identify nurse staffing groups in acute care facilities.

designThis retrospective descriptive study used a configurational approach.

methodsData from a two-month target period from January-March 2016 were collected for 40 facilities in four different hospitals in one of the largest regions of Quebec. Multiple factorial analysis and hierarchical ascendant classification were used to generate a limited number of nurse staffing groups. RESULTS/

findingsFour distinct nurse staffing groups emerged from this study. The least resourced model relied mainly on less qualified personnel and agency staff. The moderately resourced basic model was assessed as average across all staffing dimensions, but employed less overtime, relying mostly on auxiliary nurses. The moderately resourced professional group, also moderate in most variables, involved more overtime and fewer less qualified personnel. The most resourced group maximized highly qualified personnel and minimized instability in the nursing team.

conclusionThis study covered multiple staffing groups with widely varying characteristics. Most groups entailed risks for quality of care at one or more levels. Few care units approached the theoretical staffing ideal.

Indexed as

acute careadministrationgroupsnursesnursingskill mixstaffingstaff mix

Identifiers

PMID31367448
PMCPMC6650648
OpenAlexW2952874730

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.