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Record W3217000792 · doi:10.1111/jan.15108

Is Action Research just about telling a good story?

2021· editorial· en· W3217000792 on OpenAlexaff
Mary Casey, David Coghlan, Áine Carroll, Diarmuid Stokes

Bibliographic record

VenueJournal of Advanced Nursing · 2021
Typeeditorial
Languageen
FieldHealth Professions
TopicMental Health and Patient Involvement
Canadian institutionsTrinity College
Fundersnot available
KeywordsAction (physics)PsychologyMedicine

Abstract

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Patient participation demands greater attention from healthcare providers and researchers to ensure that the voice of vulnerable people is heard in a system that was originally designed to accommodate the needs of healthcare professionals. Covid 19 has brought the realisation that nursing care is not provided in a vacuum, it is impacted by socioeconomic and political mores. There is an increased demand to demonstrate the impact and outcome from participation and co-design in research (Palmer, 2020). More critical action in our research and practice is needed to collectively find solutions to such current questions as global warming and the unequal global distribution of resources such as Covid 19 vaccine allocations. The real question for us as a global society is—how can our research be of value beyond the essential development of the vaccine(s)? The patients’ voice and that of employees in healthcare have never been more crucial to ensuring future societies can get on with everyday living with Covid 19 in a meaningful way as we undergo transformative change and manage the impact of Covid 19 locally, nationally and globally. The utility and flexibility of action research have increased the interest and employability of this approach in a range of healthcare contexts, in the recent past, as healthcare systems continue to value the voice of staff and patients. Active participation of patients and staff is vital to ensure the provision of a responsive and meaningful health service. Traditional research approaches are somewhat limited in their ability to include the participants’ voice in their research journey. It is without a doubt that public and patient involvement initiatives (PPI) have created a greater understanding of the different layers of participation from collaboration, to consultation to inform the wider community, and in this onion-like layered engagement concept, action research remains the leader in the field. In this context, action research is gaining interest and becoming more popular, essentially because of ‘its participatory paradigm, which invites participants to be both embedded and reflexive in the creation of collaborative learning and of actionable knowledge where research is with, rather than on or for, people’ (Casey et al., 2021: 3). Its dual focus on theory and action has brought an increased emphasis on the application of action research in healthcare. Therefore theory and practice are linked in action research as are, thinking and taking action to achieve both practical and research objectives which provide improvement by closing the gap between research and implementation. In the context of change, action research aims at taking action and at the same time creating knowledge or theory about that action or change. In this way, action research provides actionable knowledge for other health care contexts such as learning about the management of the longer-term impacts of Covid 19. Action research therefore brings a change, by not only raising awareness but also in garnering participative support for action mediated by concerns for social justice in a local and global healthcare context. The action research process involves cycles of action and reflection and always has two goals: to find a solution to a real-world issue and to contribute to science through the elaboration of extant theory or the creation of new thinking. These are the dual imperatives of action research. However, it is the creation of actionable knowledge that is the most rigorous test of knowledge creation. Asking such questions as:-does the particular solution work in practice? and if not -why not?– and then, asking what can we do to improve the situation further? To address such questions, action research employs strict criteria and describes definite steps on how to engage in the research process. These steps are cyclical and iterative and for some researchers, these are seen as two overlapping spirals of activity, where one spiral captures the research activity and the other refers to the work project. Action research focuses on creating solutions to practical issues (see Figure 1) and it simultaneously empowers practitioners by encouraging their participation in all aspects of the research process and in its implementation. Participation in healthcare is a complex issue as it is mediated by the different lenses through which different professional groups view and understand problems while patients must engage with these in an organisational hierarchical context. It is through the relational component of action research that the participative values are to be found as it embraces multiple ways of knowing-for-action (Bradbury et al., 2019). Hence, participation from participants and co-researchers increases the involvement and sustainability of outcomes; however, the measurement of participation and the relational components is loose and inconsistent (Casey et al., 2021). In some published reports, the mention of stakeholder consultation, participant interviews and involvement in focus groups, is proffered as meeting the entire gambit of rigour to demonstrate the quality of the co-researcher relationship. This level of involvement aligns more with the idea of a consultation process, where the voice of the participant may be heard but there is no definite agreement that it will impact the direction of the project or research. Morrison and Lilford (2001) attest that, like other traditional forms of research, action research is scientific and although it is context dependent, nevertheless it offers explanatory theories, and these theories can be falsified. This raises the question of rigour and the scientific basis of action research. Action research therefore not only needs to be undertaken rigorously but also needs to demonstrate the details of how rigour was executed. The judge for rigour in action research is on the basis that knowledge generation is reflected in the co-creation and cooperation between the researcher and co-researchers through praxis. More recently, Coghlan and Shani (2014) suggest that demonstrating the quality of action research studies can be achieved by: (a) providing knowledge of the practical and academic context of the project; (b) enabling participants to become co-researchers; (c) undertaking cycles of action and reflection as the project is being implemented and knowledge is being co-generated; and (d) creating outcomes that are both achievable and sufficiently strong for theory development about the topic itself, or the process of undertaking the change or the actual change. It is essential to demonstrate not only that the research is guided by a reflexive concern for practical outcomes but also the process of iterative reflection as part of the change process. In other words, is the view of the patient or participant clearly visible? Action research must therefore acknowledge a range of perspectives on reality embodied in the different ways of knowing evident in the inclusion of the voice of participants without attempting to find consensus. The importance of the project is also considered essential criteria of quality and whether the project creates longer-term sustainable change. It is imperative therefore that all action researchers provide a rationale for their research approach and provide the evidence to support the quality of their action research studies. Without a description of such quality criteria, there is a risk that action research becomes negated as a valuable research approach. We do not have any conflicts of interest to report.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.054
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.010
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.446
GPT teacher head0.601
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations8
Published2021
Admission routes1
Has abstractyes

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