Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Introduction: Hospitals deploy temporary nurses to bridge staffing gaps. However, evidence remains inconclusive regarding the extent, patterns, and factors driving temporary deployment. This study aimed to describe how temporary nurses are deployed as a response to shift-level schedule deviations and shortfalls in planned schedules.
Methods: Our four-month time-series analysis covered 1344 shifts across two medical and two surgical units in a tertiary hospital in Iran. Shift-level data included nursing staff numbers, the skill mix, staff absences and the patient count and turnover. The patient-to-nurse ratio was used to gauge staffing levels. Data were analysed using both descriptive and analytical approaches, including the fitting of three generalized linear mixed models to assess potential drivers of shifts involving temporary RNs.
Results: Temporary nurses worked on 12.2 % of shifts with the majority being Registered Nurses (RNs) (81.7 %). Only 28.5 % of deviations led to temporary RN deployments. While students and aides were sometimes reallocated to fill absences, the majority of absences (57.1 %) went unaddressed. Temporary staff mainly worked on shifts with below-average RN-staffing. Unit-level deployment rates varied widely (3.6 %-55.9 %). Model 1 revealed that RN absence increased the odds of using a temporary RN by 2.14 times (AIC = 785.1). Model 2 indicated that each additional patient, increased the odds by 11 % (AIC = 740.7). Model 3 showed that when RN-staffing was below-average the odds of using a temporary RN were 3.96 times higher than the average level (AIC = 707.4).
Conclusion: Temporary nurse deployment was relatively infrequent. While temporary nurses were strategically deployed to address understaffing and short-notice deviations, their deployment did not fully bridge the staffing needs. On high-demand units, temporary staff were commonly supplemented by reallocating students. Some temporary deployments occurred even where RN-staffing was at an average level. These findings indicate an urgent need to enhance the effectiveness of temporary deployment and optimize workforce resources to ensure high-quality care.
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Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC12410400 | PMC |
http://dx.doi.org/10.1016/j.ijnsa.2025.100383 | DOI Listing |