Evidence map›Paper›PMID 35755424›Full record

ArticleInternational journal of intercultural relations : IJIR2022

Torn between two worlds: COVID, it's your fault.

Lyn Lewis, Parisa Tadi, Anienie Veldsman, Jyoti Jhagroo, Celeste Harrington, Martha McFaul

Abstract read
In one paragraph

Article in International journal of intercultural relations : IJIR, 2022. 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
–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

0 citing papers in PubMed.

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

6 authors.

Lyn LewisAuckland University of Technology, New Zealand.
Parisa TadiAuckland University of Technology, New Zealand.
Anienie VeldsmanAuckland University of Technology, New Zealand.
Jyoti JhagrooAuckland University of Technology, New Zealand.
Celeste HarringtonAuckland University of Technology, New Zealand.
Martha McFaulAuckland University of Technology, New Zealand.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic has had a global reach, impacting each one of us to varying degrees. Research is emerging on the medical, educational, social, and psychological effects of this pandemic; however, little has appeared yet on the impact on immigrant acculturation. We are six higher education immigrant academics in New Zealand and our collaborative autoethnographic study reports on the disruption to our immigrant selves caused by COVID-19. We present findings from our narratives written at two different times: a reflection after the initial eight-week New Zealand lockdown from March to May 2020, and a second meta-reflection one year later. We also illustrate, in graph form, our perceived stress levels associated with being immigrants during COVID-19. The narratives describe strong emotions linked to transnational connections that bound us to loved ones at home in COVID-ravaged countries. While we describe a stronger sense of belonging to our new society, we also identify COVID-19 as a disruptor and interrupter of the acculturation process regardless of our length of settlement in New Zealand. We argue that the increased stress of COVID-19 has triggered an interruption or oscillation that has disrupted our acculturation trajectories, surfacing emotions of acculturative stress even for those well adapted to their new society. These findings may resonate with immigrants in similar contexts and circumstances.

Indexed as

Acculturation trajectoryAcculturative stressAutoethnographyCOVID-19ImmigrantTransnational relationships

Identifiers

PMID35755424
PMCPMC9212626

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.