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Record W4387002069 · doi:10.1111/jan.15877

Understudied phenomena and emerging methodologies in nursing and midwifery: What's new on the horizon?

2023· editorial· en· W4387002069 on OpenAlexaff
Lorelli Nowell, Martina Giltenane

Bibliographic record

VenueJournal of Advanced Nursing · 2023
Typeeditorial
Languageen
FieldSocial Sciences
TopicCriminal Justice and Corrections Analysis
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsNursingHealth carePsychological interventionNursing researchNurse educationMedicinePolitical science

Abstract

fetched live from OpenAlex

This special issue, guest edited by the Journal of Advanced Nursing's inaugural Early Career Researcher Editorial Board, intends to bring to the forefront some of the historically understudied phenomena and emerging methodologies that are relevant and of interest to nurses and midwives. Nurses and midwives play a pivotal role in advancing healthcare, improving health outcomes, creating better health systems and fostering evidence-based practices. Over the years, nursing research has witnessed significant transformations, with the emergence of new phenomena and methodologies that have the potential to reshape the landscape of nursing and midwifery practice. This special issue illuminates important research impacting nursing and midwifery practice and education. Studies included in this special issue have a distinct focus on work that explicitly articulates advancements for nursing and midwifery knowledge development, practice, education, research or policy. Health disparities among different demographic groups continue to be a significant challenge in healthcare and a growing area of interest. Nursing research is increasingly focused on identifying the root causes of health inequalities and developing targeted nursing interventions to address them. For example, included in this special issue are studies about vulnerable populations including prisoners (Bright et al., 2023), abused women (Halldorsdottir, 2023) and those experiencing housing instability (Robinson et al., 2023). From the perspective of female survivors, Halldorsdottir (2023) looked at the impact male intimate terrorism had on survivors. Halldorsdottir (2023) concluded that nurses need to be aware of the danger and be able to screen for intimate terrorism. Nurses need to have the knowledge and skills to be able to provide trauma-focussed care to women who have been victims of such trauma (Halldorsdottir, 2023). A mapping and review synthesis conducted by Bright et al. (2023) provided a snapshot of how sporadically articles in relation to nursing in the prison context are published in nonspecialist/generic nursing journals highlighting the necessity for increasing publication of nursing in the prison context with the aim of reducing stigma and marginalization of prisoners or people working in prisons (Bright et al., 2023). Robinson et al. (2023) described how pregnancy health among birthing and postpartum people is impacted by housing instability. Their study highlights the immediate social determinant needs of birthing people and reiterates the need for a more thorough assessment of need in the prenatal setting. By further exploring these traditionally understudied phenomena for vulnerable populations, nurses can advocate for equitable healthcare access, deliver more culturally competent patient-centred care and help drive positive health outcomes. Nursing is a demanding and multifaceted profession that extends beyond the confines of caring for others. While the crucial role of nurses in healthcare is well-acknowledged, there are several underexplored areas of nursing practice that are examined in this special issue. For example, the experiences of disabled nurses (Baker et al., 2023), nurse-led models of care (Bassah et al., 2023), and the impact of varying interferences on nurses' working memory (Hu et al., 2023). In a scoping review, Baker et al. (2023) identified the varying workplace experiences of nurses and midwives with disabilities. They illuminated that nurses and midwives can be greatly affected by disability and highlighted the importance of diversity, equity, accessibility and inclusion across the profession (Baker et al., 2023). A scoping review by Bassah et al. (2023) mapped evidence in low- and middle-income countries in relation to the use of nurse-led palliative care models for adults with life-limiting illnesses. They highlighted that nurse-led care models can improve the quality of life of patients with life-limiting illnesses and improve access to services in low- and middle-income countries (Bassah et al., 2023). Using a repeated measures design, Hu et al. (2023) explored the role of attention control and the impact varying types of interference had on nurses' working memory. They found that interruptions and distractions had varying effects on nurses' working memory and suggested ways to reduce the negative effect interference has on nurses, in order to enhance work efficiency and reduce patient risk (Hu et al., 2023). These understudied issues present complex challenges that profoundly affect nurses and, consequently, patient care. To elevate and ensure the sustainability of the nursing profession, nursing researchers are delving into these topics, seeking innovative solutions that prioritize the well-being of nurses, enhance workplace conditions and foster inclusivity. Additionally, this special issue highlights unique ways of studying well-established phenomena, including methodologies borrowed and adapted from other disciplines, as well as new methodologies with nursing as the ontological base. As nursing research advances, emerging methodologies are playing a pivotal role in reshaping the future of healthcare. The adoption of clique percolation (Lee et al., 2023) and netnography methods (Smith et al., 2023) may help uncover new understandings of important nursing phenomena. Lee et al. (2023) carried out a cross-sectional study identifying clique percolation, a joint community detection algorithm in network science, which can detect overlapping communities in real networks. Smith et al. (2023) critically reflected on their team's experience of using netnography to study parents and pregnant women who are vaccine-hesitant, using an underutilized and innovative methodology in nursing research. Furthermore, novel methods to convert qualitative data into quantitative values (Halevi Hochwald et al., 2023) or reduce retraumatization of researchers and nurses taking part in important longitudinal studies (Conolly et al., 2023) hold great promise for the nursing profession. Halevi Hochwald et al. (2023) described the benefits and methods of translating qualitative data to quantitative values using a matched mixed method research design. They suggested that this methodology allows for further valuable discussion within research papers and allows better integration and easier presentation of results (Halevi Hochwald et al., 2023). Conolly et al. (2023) critically evaluated the concepts of harm and retraumatization in the research process and explored the ethical consequences of carrying out research in relation to distressing topics. Their study accentuated the value of giving autonomy to research participants to control what they want to say within a supportive research team while having reflexivity and debriefing central in the process (Conolly et al., 2023). By embracing the emerging phenomena studied and methodologies uncovered in this special issue, nurses and midwives can continue to be at the forefront of driving positive healthcare transformation. Through collective efforts and a commitment to exploring understudied health disparities, issues affecting nursing and midwifery practice, and emerging methodologies, nurses and midwives can continue to lead the way towards a healthier and more equitable society. Lorelli Nowell and Martina Giltenane conceived the paper, contributed to key background literature and knowledge on understudied phenomenon and emerging methodologies, drafted the manuscript, edited the manuscript and read and approved the final manuscript. Lorelli Nowell is the guarantor of this manuscript. We acknowledge and thank Dr. Debra Jackson for her guidance with this special edition. The authors have no funding to declare. The authors have declared no conflict of interest. Data sharing is not applicable to this article as no new data were created or analyzed in this article.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.031
metaresearch head score (Gemma)0.103
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.969
Threshold uncertainty score0.166

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0310.103
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0070.005
Science and technology studies0.0090.015
Scholarly communication0.0330.018
Open science0.0060.009
Research integrity0.0140.028
Insufficient payload (model declined to judge)0.0090.004

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 head0.061
GPT teacher head0.421
Teacher spread0.359 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainMethods
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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Citations0
Published2023
Admission routes1
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