Journal of Community & Public Health Nursing
Open Access

The local and national government control required for professional practice to avoid the risk of harm to the public if practiced by an unprepared or incompetent person is defined as health profession regulation. We use the term "regulatory" to refer to any activities that are linked to this concept in this work. Qualitative research is critical for providing exploratory and explanatory data in conjunction with quantitative results, whether examining the consequences of regulations on stakeholders or bringing their voice into the development of regulations around health issues such as patient safety, credentialing, competency, scope of practise, transitions to practise, disciplinary actions, and so on. When investigating nursing and other professions, qualitative research is critical in ensuring that stakeholders' voices are heard and that their experiences inform the review of rules and policies. Despite the need for reliable qualitative evidence on regulatory concerns, the Journal of Nursing Regulation only receives a small number of qualitative researches that are sufficiently rigorous and trustworthy to be published. According to a survey of published studies, only 12 qualitative studies have been published in the nearly ten years since the journal's start, compared to over 300 quantitative studies, accounting for less than 5% of the total evidence. This article provides an outline of major methodological problems researchers may face while conducting qualitative research on nursing and health profession regulation concerns in order to encourage high-quality qualitative submissions on regulatory issues. Education that never ends [1].

Because most researchers did not comprehend the norms for qualitative research rigour, qualitative studies have been criticized in the past. Experts created and improved four essential concepts to assist assure the rigour of qualitative research findings to satisfy this need [2].

In qualitative research, rigour refers to how the researcher determines the reliability of the findings. Qualitative research that is thorough reflects how well the study was implemented and how well it handled unforeseen problems. It contains four well-defined dimensions that correspond to conceptual counterparts in quantitative research. the terminology used to establish rigour in qualitative research projects, as well as their definitions and quantitative equivalents While Morse contends that qualitative and quantitative rigour nomenclature should be the same, qualitative rigour vocabulary was developed over 30 years ago. They have influenced the language of the field. As a result, explaining directly or implicitly how the researcher secured the study's rigour is an important aspect of the dissemination process for most qualitative investigations [3]. Tracy’s eight criteria for "good" qualitative research provide authors with clear direction on how to improve the rigour of their investigations. Bias reduction is an important part of assuring rigour in any research project. Bias is an important issue in qualitative research because it refers to the key worry about the potential influence of the research study's researchers' beliefs and opinions. How can the reader know, for example, that the researchers were as objective as possible during the inter-view process? How much do readers know about the outcomes analysis process? Bias mitigation is an integral aspect of the qualitative data analysis process. When researchers raise leading questions during interviews or impose viewpoints during the analysis process, they are directing the findings to fit their own beliefs rather than remaining objective. It's the same thing that happened with when quantitative researchers manipulate statistical analyses to get the desired outcomes rather than the real ones. Furthermore, reducing bias helps to assure study reproducibility, which is a major issue for most funding agencies [4].

Data saturation is also used to establish qualitative rigour. When a researcher has performed enough interviews, no new information is gained from participants, this is known as data saturation. In most published qualitative studies, it is frequently underreported. In qualitative research, data saturation is important since it signals when no more interviews are required and data collecting may be completed. Poorly planned qualitative studies budget just for a particular number of interviews and anticipate that data saturation will occur within that number, rather than extending the investigation until data saturation is achieved [5]. It is crucial for researchers to manage language barriers between participants and themselves in international studies or those with stakeholders who may not speak the same language as the researcher. Due to the possibility of translation errors, data is at risk for semantic, conceptual, technological, and content-related difficulties. Poorly translated interview data for qualitative data means findings are untrustworthy, or, as in the quantitative terminology, not dependable or valid. These risks can be mitigated by using qualified interpreters, multi-language coding methods, and independent checks on the translation and coding processes.

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1. Globe D, Young NL, Von Mackensen S, Bullinger M, Wasserman J, et al. (2019) Measuring patient-reported outcomes in haemophilia clinical research. Haemophilia 15(4):843-52.

Indexed at, Google Scholar, Crossref

2. Riva S, Bullinger M, Amann E, von Mackensen S (2010) Content comparison of haemophilia specific patient-rated outcome measures with the international classification of functioning, disability and health (ICF, ICF-CY). Health Qual Life Outcomes 8:139.

Indexed at, Google Scholar, Crossref

3. Szende A, Schramm W, Flood E, Larson P, Gorina E, et al.(2003) Health-related quality of life assessment in adult haemophilia patients: a systematic review and evaluation of instruments. Haemophilia 9(6):678-87.

Indexed at, Google Scholar, Crossref

4. Groenvold M (2010) Health-related quality of life in early breast cancer. Dan Med Bull 57(9):4184.

Indexed at, Google Scholar, Crossref

5. Von Mackensen S, Campos IG, Acquadro C, Strandberg-Larsen M (2013) Cross-cultural adaptation and linguistic validation of age-group-specific haemophilia patient-reported outcome (PRO) instruments for patients and parents. Haemophilia 19(2):73-83.

Indexed at, Google Scholar, Cross ref