Evidence map›Paper›PMID 35262311›Full record

ArticleHealth promotion and chronic disease prevention in Canada : research, policy and practice2022

Conducting research during the COVID-19 pandemic: the Op LASER study.

Deniz Fikretoglu, Megan Thompson, Tonya Hendriks, Anthony Nazarov, Kathy Michaud, Jennifer Born, Kerry A Sudom, Stéphanie Bélanger, Rakesh Jetly

Abstract read
In one paragraph

Article in Health promotion and chronic disease prevention in Canada : research, policy and practice, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. From pandemic to endemic: learning from the past and preparing for the future.Health promotion and chronic disease prevention in Canada : research, policy and practice · 2022
    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

9 authors.

Deniz FikretogluDefence Research and Development Canada, Toronto Research Centre, Toronto, Ontario, Canada.
Megan ThompsonDefence Research and Development Canada, Toronto Research Centre, Toronto, Ontario, Canada.
Tonya HendriksDefence Research and Development Canada, Toronto Research Centre, Toronto, Ontario, Canada.
Anthony NazarovHumanSystems Inc., Guelph, Ontario, Canada.
Kathy MichaudDirector General Military Personnel Research and Analysis, Ottawa, Ontario, Canada.
Jennifer BornDirector General Military Personnel Research and Analysis, Ottawa, Ontario, Canada.
Kerry A SudomDirector General Military Personnel Research and Analysis, Ottawa, Ontario, Canada.
Stéphanie BélangerRoyal Military College of Canada, Kingston, Ontario, Canada.
Rakesh JetlyCanadian Forces Health Services Group, Ottawa, Ontario, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The Canadian Armed Forces (CAF) deployed 2595 regular and reserve force personnel on Operation (Op) LASER, the CAF's mission to provide support to civilian staff at longterm care facilities in Ontario and in the Centres d'hébergement de soins de longue durée in Quebec. An online longitudinal survey and in-depth virtual discussions were conducted by a multidisciplinary team of researchers with complementary expertise. This paper highlights the challenges encountered in conducting this research and their impact on the design and implementation of the study, and provides lessons learned that may be useful to researchers responding to similar public health crises in the future.

Indexed as

COVID-19HumansLasersOntarioPandemicsSARS-CoV-2mental healthmilitary personnelmoral injury

Identifiers

PMID35262311
PMCPMC9022956

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.