Evidence map›Paper›PMID 39628948›Full record

ArticleBritish paramedic journal2024

Using an analytic auto-netnographic approach to explore the perceptions of paramedics in primary care.

Georgette Eaton, Stephanie Tierney, Geoff Wong, Kamal R Mahtani

Abstract read
In one paragraph

Article in British paramedic journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. 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.

Georgette EatonNuffield Department of Primary Care Health Sciences ORCID iD: https://orcid.org/0000-0001-9421-2845.
Stephanie TierneyNuffield Department of Primary Care Health Sciences ORCID iD: https://orcid.org/0000-0002-2155-2440.
Geoff WongNuffield Department of Primary Care Health Sciences ORCID iD: https://orcid.org/0000-0002-5384-4157.
Kamal R MahtaniNuffield Department of Primary Care Health Sciences ORCID iD: https://orcid.org/0000-0002-7791-8552.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Paramedics in the UK are moving from emergency ambulance services into primary care, where they are employed to boost the clinical workforce. Whereas there is emerging research that seeks to understand the contribution of paramedics to the primary care workforce, there is none regarding the perceptions paramedics have regarding their role in primary care. Methods: An analytic auto-ethnography was undertaken, utilising a peripheral membership approach for online communities used by paramedics on Facebook, Reddit and Twitter (now X). Over a 3-month period (December 2021 to February 2022), the primary researcher reflected on the conversations, comments and opinions posted within these communities within a reflexive (immersion) journal, considering them against the context of her own experience. Results: Paramedics in primary care, who are generally isolated due to their geographical isolation from each other, utilise online social spaces to foster a community of practice. These forums are used to discuss their clinical role, education and experiences, as well as to consider their place within the primary care workforce. Conclusion: This is the first application of this methodology within online social spaces utilised by UK paramedics. This article also presents novel use of a peripheral membership approach within an analytic auto-netnography in public online spaces for researcher-practitioners.

Indexed as

community of practiceparamedicsprimary health caresocial media

Identifiers

PMID39628948
PMCPMC11610540

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.