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Published Paper Details
Paper Title:
A descriptive study to assess the knowledge and self-efficacy regarding ABG analysis and its Interpretations among Nursing Personnel working in selected hospitals of Ambala, Haryana
Background- Arterial blood gas analysis is an essential part of diagnosing and managing a patient’s oxygenation status and acid-base balance. Nurse as a primary care giver play an important role in early detection of high risk clients with acid-base imbalance in critical care units. Therefore, study aimed to assess the level of knowledge, self-efficacy among Nursing Personnel working in the Hospital of Ambala Haryana.
Methodology: A Descriptive Research design was adopted to conduct study among 300 Nursing Personnel which were selected by using a non-probability convenient sampling technique. Along with the assessment of Socio-demographic variables, Structured Knowledge Questionnaire and Modified Arterial Puncture Self Efficacy Scale (MAPSES) was also used to assess the knowledge & self- efficacy regarding ABG analysis and its interpretation among Nursing Personnel. Data was analysed by using descriptive and inferential statistics in SPSS software version 20.
Results: More than half of Nursing Personnel (80%) were having good level of knowledge regarding ABG analysis and its interpretation and maximum Nursing Personnel (81.7%) were having high level of self-efficacy regarding ABG analysis and its interpretation.
Keywords:
Knowledge, Nursing Personnel, Self-efficacy.
Cite Article:
"A descriptive study to assess the knowledge and self-efficacy regarding ABG analysis and its Interpretations among Nursing Personnel working in selected hospitals of Ambala, Haryana", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.8, Issue 10, page no.83 - 88, October-2023, Available :http://www.ijrti.org/papers/IJRTI2310014.pdf
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ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator