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Record W4241039328 · doi:10.1111/nicc.12506

What's in this issue

2020· article· en· W4241039328 on OpenAlexaboutno aff
Lyvonne N. Tume, Josef Trapani

Bibliographic record

VenueNursing in Critical Care · 2020
Typearticle
Languageen
FieldMedicine
TopicRespiratory Support and Mechanisms
Canadian institutionsnot available
Fundersnot available
KeywordsWorkforcePaediatric intensive care unitMedicinePandemicNursingIntensive careCoronavirus disease 2019 (COVID-19)Intensive care unitFamily medicinePediatricsIntensive care medicinePolitical science

Abstract

fetched live from OpenAlex

This issue of the journal sees more of a paediatric focus, with a guest editorial and two research papers focussing on different issues related to paediatric intensive care unit (PICU) nursing. In some countries, paediatric and adult intensive care unit (ICU) colleagues are more aligned and meet regularly at scientific joint meetings,1 whereas in others, they are almost entirely separate. Moreover, in some countries, children and adults are still managed in the same unit.2 There is no perfect model, and skills-wise intensive care is intensive care, whether it is delivered to a 4-week-old infant, a 15-year-old child, or a 60-year-old adult. However, this does not mean that there should be generic ICU education programmes for nurses as, although the skills may be similar, the application of these skills differs for children of different ages, as does the pathology of the patients.3 However, as a nursing workforce, we (both paediatric and adult ICU) are all highly trained and skilled, and many of these skills are transferable. At this moment in time, with a pandemic of COVID-19, this issue has never been more important. PICU nurses must, where possible, step up and assist adult ICU colleagues as they would assist PICU colleagues if COVID-19 predominantly affected children. Our guest editorial,4 written by Anne-Sylvie Ramelet, Professor of Paediatric Nursing and a PICU nurse in Switzerland, highlights an increasing issue in PICU—that of “long-stay” PICU patients, what this means, how they are defined, and the implications for PICU nurses. Despite the median length of stay of children in UK PICUs being 3 days,5 the group of children who may stay weeks, months, and sometimes years is increasing, reflecting an increasing comorbid population. Thus, this editorial is very topical and will share issues that are perhaps similar to that of adult ICU colleagues. Unplanned readmissions to a PICU have worse outcomes,6 and being able to identify children “at risk” for readmission can help us target these children and potentially impact outcomes. Konishi and colleagues7 investigated the incidence and risk factors for readmission to PICU in a single Japanese unit within 7 days of discharge over a 4-year period. They found that only 2.5% of children were readmitted to PICU, with a median readmission time of 3.5 days post-discharge. They noted three significant risk factors for readmission: an initial emergency (unplanned) PICU admission, initial admission from a general ward area, and withdrawal syndrome during their stay. They concluded that one of these factors (iatrogenic withdrawal) was potentially preventable. This is interesting because the complexity of withdrawal assessment in the paediatric population has been demonstrated,8 but we must consider this further, with targeted efforts to reduce iatrogenic withdrawal. Family-centred care (FCC) is the foundation of paediatric nursing, but delivering this in a PICU can be challenging.9 Freschette and colleagues10 conducted a qualitative study over 6 months exploring PICU nurses' lived experiences about delivering FCC before, during, and after a significant unit transformation project (to optimize unit layout and unit geography to improve FCC) in a single Canadian PICU. Data were collected over 6 months using multiple methods: participant observation, photographs, interviews, and document analysis. They found that, despite an improved environment for delivered FCC and more family involvement, nurses continued to be child-centred in their approach. Nurses exhibited both pride (in their new FCC environment) and prejudice in their negotiations with families. They concluded that solely changing the physical PICU environment was not enough to change the way that nurses practice FCC. The study by Oduyale et al11 used focus groups to explore ICU nurses' views and perspectives about the concurrent administration of multiple intravenous medications through the same lumen. The main challenges were related to the absence of compatibility data and insufficient venous access, leading the nurses to request additional venous access, swapping infusion lines, changing medication forms, and prioritizing infusions. Apart from collecting data directly from frontline clinicians, an important contribution of the study is its use of the Functional Resource Analysis Method (FRAM) to provide a visual representation of all the activities and the multiple factors associated with the process of intravenous medicine co-administration. The study was limited to 20 nurses from two hospitals in England, but its findings should prompt hospitals to ensure that compatibility charts are readily available and updated to include data for all frequently used medications and to cater for the co-administration of three (rather than just two) medications. More extensive use of the FRAM should also be considered to identify potential risks associated with having to circumvent limited resources and inadequate venous access. This paper also promotes enhanced interprofessional collaboration between pharmacists and nurses in a critical care context. The Braden Scale for Predicting Pressure Ulcer Risk is one of the most widely used pressure ulcer risk assessment tools in critical care settings12; yet, previous studies assessing its predictive value produced mixed results. This prompted Wei et al13 to conduct a systematic review and meta-analysis of previous studies investigating the predictive validity of the Braden Scale for adult ICU patients. Their comprehensive search in English and Chinese health science databases and in the grey literature led to 11 studies with a combined total of more than 10 000 patients. The pooled results indicated high sensitivity but low specificity, suggesting that the scale is useful to identify ICU patients who are at risk of pressure ulcer development but much less efficient in identifying those who are not. This overall moderate predictive value suggests the need for further adaptation of this tool to the critical care setting or the development of new tools with a higher predictive power. The next paper tackles “Failure to Rescue” and presents a qualitative service evaluation aimed at eliciting the factors that facilitate and hinder the escalation of care for deteriorating acute ward patients. Ede and her colleagues14 conducted 55 hours of qualitative observations, accompanied by ad hoc interviews, in several wards at two hospital sites in a UK National Health Service Trust. Field notes were analysed thematically, iteratively, and reflexively. The study captures complex and nuanced elements influencing the identification and timely management of deterioration and contributes to the body of knowledge about the human factors that impact decision-making and escalation of care. The findings outline the value and the limitations of Early Warning Scores (EWS) in escalating care in particular clinical scenarios. Although EWS are based on objective observations, it is evident that clinical judgement plays a crucial role in their interpretation, which may lead to the avoidance of unnecessary escalation but also to crucial delays when escalation is required. The clear audit trail provided in this paper should be helpful to guide similar service evaluations and research in other settings, particularly in terms of addressing the methodological and ethical considerations underpinning ethnographic work in acute clinical settings. It is increasingly being recognized that critical illness may have prolonged negative psychosocial consequences on both the patients and their family.15 Indeed, this journal included several papers on this topic, including a study on family functioning during and after critical illness,16 in its recent special issue on the psychological impact of the ICU environment. Yet, the specific impact of transferring critically ill patients from rural settings to distant advanced care facilities on the patients' family has received much less attention. The integrative literature review of six quantitative and qualitative studies by Burns and Petrucka in this issue17 is a welcome effort to address this gap. It is not surprising that stress and anxiety emerged as central to the rural family members' experience of such inter-facility transfers. What is, perhaps, more significant is the finding that these are mainly associated with modifiable factors, namely, the family members' physical proximity to the patient, the financial burden associated with the transfer, the family's access to information and support networks, and—crucially—the actions of health professionals. These findings should prompt ICU practitioners and managers to develop, implement, and evaluate interventions to support such families. Future research in this area could focus on the long-term consequences of an inter-facility transfer on the family and on the experience of family members who are “left behind” during inter-facility transfers. The range of papers in this issue demonstrates yet again the depth and breadth of critical care nursing expertise and interests across the globe. We hope you find them interesting and inspiring and that they serve to remind us all that only we, as critical care nurses, can advance nursing science, and it is us who need to ask the important questions and seek to answer these questions. Finally, we would like to take the opportunity of the paediatric focus of this issue to announce that we are now also welcoming the submission of manuscripts on neonatal intensive care nursing in order to represent critical care nursing throughout the lifespan.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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

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 head0.079
GPT teacher head0.410
Teacher spread0.330 · 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 designOther design
Domainnot available
GenreEmpirical

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
Published2020
Admission routes1
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