PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 15951707

Nurses' views on work enabling factors.

Marianne McLennan
View original source ↗Export metadata ↓

Abstract

OBJECTIVE: In this study nurses' views on work enabling factors, priority issues for improvement, and stress and satisfaction levels were examined. BACKGROUND: This research builds on previous work studies that empirically established work dimension indices related to individual and organizational outcomes. A survey tool was adapted so it replicated previous instrument development research, as well as explored its practicality as a benchmark and process tool in healthcare organizations. METHODS: A descriptive design was used in a single institution employing a self-report survey. RESULTS: All of the enabling factors were found to be important to nurses. The lowest-rated factors related to manager-staff relationships, congruent with open-ended responses that identified improving management as the highest priority. Unlike previous studies, job insecurity and trust were not issues, and nurses predominantly (92%) reported that quality care was provided. CONCLUSIONS: A simple enabling index and survey tool can be used to proactively assess work environments, so that conditions can be improved before they lead to morale, sickness, and retention problems. The survey results provide important feedback that can prompt discussions about how workplaces can become healthier, more productive, and rewarding places for nurses.

Explore related subjects

HumansJob SatisfactionNursesStress, PsychologicalSurveys and QuestionnairesWorkplaceJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Marianne McLennan. 2005. Nurses' views on work enabling factors.. https://doi.org/10.1097/00005110-200506000-00008

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2027

Global Genomic Surveillance.

Global genomic surveillance has emerged as a foundational pillar of public health in the twenty-first century, enabling real-time tracking of pathogen evolution and informing outbreak response. This chapter examines the strategic architecture of global genomic surveillance, focusing on its application to arboviruses such as chikungunya virus (CHIKV). It explores the integration of genomic data with epidemiological, clinical, and environmental information within a One Health framework, while addressing critical challenges in governance, equity, and interoperability. The discussion covers the entire genomic surveillance workflow, from sample collection and sequencing to bioinformatic analysis and phylogenetic inference, and highlights the transformative role of artificial intelligence (AI) in predictive surveillance. By analyzing global initiatives, operational barriers, and emerging technologies, this chapter underscores the necessity of sustainable, equitable, and interoperable genomic systems to proactively address current and future infectious disease threats.

Humans↗
PubMed2027

Systematic Dissection of Key Driver Perturbation Signatures in Single Cells via ECCITE-seq.

CRISPR screens, such as expanded CRISPR-compatible cellular indexing of transcriptomes and epitopes by sequencing (ECCITE-seq), enable the simultaneous measurement of transcriptomes, gRNA identity, and cell-surface protein expression at single-cell resolution to systematically interrogate gene function. This platform provides a powerful and scalable experimental approach for validating disease-associated regulators identified by large-scale association studies and other computational methods, including network-based analyses of multi-omics data. Here, as an example application, we describe an ECCITE-seq framework to characterize the transcriptomic consequences of perturbing multiple neuronal key driver genes associated with Alzheimer's disease (AD) in human-induced pluripotent stem cell (hiPSC)-derived neurons. More broadly, by integrating customized pooled gRNA libraries with different CRISPR effectors across multiple cell types, this approach allows for the assessment of the regulatory impact of candidate genes implicated in development and disease processes.

Humans↗
PubMed2027

Identification of Genome-Wide Chromatin Structural Aberration in Cancer by Hi-C Analysis.

Aberrant three-dimensional genome organization is a hallmark of cancer, often driving oncogene activation through mechanisms such as enhancer hijacking. High-throughput chromosome conformation capture (Hi-C) maps these interactions on a genome-wide scale. Unlike earlier dilution-based methods, in situ Hi-C performs proximity ligation within intact nuclei, minimizing random ligation noise and enabling fine-scale structure detection. This chapter describes an optimized in situ Hi-C protocol tailored for cancer cell lines using MboI digestion and biotin-mediated pull-down to generate high-complexity libraries. We further outline a computational workflow that extends beyond standard topological mapping of compartments and topologically associating domains to identify cancer-specific aberrations. Specifically, we focus on detecting chromosomal rearrangements (structural variants) and characterizing the distinct circular topology of extrachromosomal DNA. This integrated experimental and analytical framework provides the necessary tools to dissect the spatial dysregulation underlying tumor evolution.

Humans↗