Evidence map›Paper›PMID 30541471›Full record

ArticleBMC medical research methodology2018

Participatory action research, mixed methods, and research teams: learning from philosophically juxtaposed methodologies for optimal research outcomes.

Marguerite C Sendall, Laura K McCosker, Alison Brodie, Melissa Hill, Phil Crane

Abstract read
In one paragraph

Article in BMC medical research methodology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 citing papers in PubMed.

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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

5 authors.

Marguerite C SendallSchool of Public Health and Social Work / Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Victoria Park Road, Kelvin Grove, QLD, 4159, Australia. m.sendall@qut.edu.au.ORCID 0000-0002-1239-9173
Laura K McCoskerSchool of Public Health and Social Work / Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Victoria Park Road, Kelvin Grove, QLD, 4159, Australia.ORCID 0000-0001-9107-9401
Alison BrodieSchool of Public Health and Social Work / Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Victoria Park Road, Kelvin Grove, QLD, 4159, Australia.ORCID 0000-0003-2786-8152
Melissa HillSchool of Public Health and Social Work / Institute of Health and Biomedical Innovation, Queensland University of Technology (QUT), Victoria Park Road, Kelvin Grove, QLD, 4159, Australia.ORCID 0000-0001-8077-0889
Phil CraneSchool of Social Science, University of the Sunshine Coast, Room D1.32, Locked Bag 4, Maroochydore DC, QLD, 4558, Australia.ORCID 0000-0001-9105-1089

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWorkplace health interventions incorporating qualitative and quantitative components (mixed methods) within a Participatory Action Research approach can increase understanding of contextual issues ensuring realistic interventions which influence health behaviour. Mixed methods research teams, however, face a variety of challenges at the methodological and expertise levels when designing actions and interventions. Addressing these challenges can improve the team's functionality and lead to higher quality health outcomes. In this paper we reflect on the data collection, implementation and data analysis phases of a mixed methods workplace health promotion project and discuss the challenges which arose within our multidisciplinary team.

methodsThis project used mixed methods within a Participatory Action Research approach to address workers' sun safety behaviours in 14 outdoor workplaces in Queensland, Australia, and elucidate why certain measures succeeded (or failed) at the worker and management level. The project integrated qualitative methods such as policy analysis and interviews, with a range of quantitative methods - including worker surveys, ultraviolet radiation (UVR) exposure measurement, and implementation cost analyses.

resultsThe research team found the integration of qualitative and quantitative analyses within the Participatory Action Research process to be challenging and a cause of tensions. This had a negative impact on the data analysis process and reporting of results, and the complexity of qualitative analysis was not truly understood by the quantitative team. Once all researchers recognised qualitative and quantitative data would be equally beneficial to the Participatory Action Research process, methodological bias was overcome to a degree to which the team could work cooperatively.

conclusionsMixed methods within a Participatory Action Research approach may allow a research team to discuss, reflect and learn from each other, resulting in broadened perspectives beyond the scope of any single research methodology. However, cohesive and supportive teams take constant work and adjustment under this approach, as knowledge and understanding is gained and shared. It is important researchers are cognisant of, and learn from, potential tensions within research teams due to juxtaposed philosophies, methodologies and experiences, if the team is to function efficiently and positive outcomes are to be achieved.

Indexed as

Qualitative ResearchHealth PromotionHealth Services ResearchHumansInterdisciplinary CommunicationOccupational ExposureOccupational HealthOutcome Assessment, Health CarePrimary Health CareQueenslandResearch DesignResearch PersonnelUltraviolet RaysWorkplaceMixed methodsOutdoor workersParticipatory action research (PAR)QualitativeQuantitativeResearch paradigmsSun protectionWorkplace health promotion

Identifiers

PMID30541471
PMCPMC6290508

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.