A Life Course Approach to Understanding Job Satisfaction and Turnover Intentions Among Nursing Home Staff: A Mixed Method Study

Nursing homes continue to experience persistent challenges in retaining nursing home (NH) staff. In this dissertation, I argue that understanding the life course context of nursing home staff has the potential to shed needed light on this persistent issue. The life course perspective emphasizes understanding the dynamic interplay of personal, social, organizational, and historical factors and how this dynamism influences lives. I use a mixed method research design allowing me to analyze cross-sectional surveys (n=678, 23 NHs) and interview data (n=25, 2 NHs) complementing each other at various phases of this dissertation. I use multiple linear and ordinal logit regressions to estimate predictive models consisting of demographic, job quality, and life course factors influencing job satisfaction and employment intentions. I use modified grounded theory and constant comparative methods to interpretively identify themes in the interview data, leading to 20 major themes. Life course-relevant findings include intergroup differences in employment intentions by life course markers and identities, e.g., gender, race, marital status, life course stage, and career stage, demonstrating a significant impact on job satisfaction and intent to stay. Life course contingencies such as benefits, and breadwinner status also significantly influence job satisfaction and intent to stay. Further, I find evidence that cumulative disadvantages, job attitude, professional learning, equality and fairness, supportive leadership, emotional burnout and care concerns have additional significant impacts on both job satisfaction and intent to stay. Findings from the analysis of interviews lend support to the quantitative findings. This study has practical implications for nursing home leadership and policy makers. Nursing home leaders must amend their conventional strategies of retaining the LTC workforce by creating tailored support for workers while addressing job quality deficits. Access to mental health services, assessment of turnover risks at various life courses and career stages, professional support, increased leave for caregiving and respite, improved access to high-quality health insurance and benefits that assist this workforce in dealing with contingency factors and career mobility (e.g., small grants, tuition remission) have the potential to alleviate some of the instability in this often-vulnerable workforce.

Publication Details

Journal
Georgia State University Repository
Published
2025-07-15
DOI
https://doi.org/10.57709/e122-4922
Primary Topic
Geriatric Care and Nursing Homes
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Life Course Approach to Understanding Job Satisfaction and Turnover Intentions Among Nursing Home Staff: A Mixed Method Study

Georgia State University Repository
Geriatric Care and Nursing Homes
article

A Life Course Approach to Understanding Job Satisfaction and Turnover Intentions Among Nursing Home Staff: A Mixed Method Study

article en

Abstract

Nursing homes continue to experience persistent challenges in retaining nursing home (NH) staff. In this dissertation, I argue that understanding the life course context of nursing home staff has the potential to shed needed light on this persistent issue. The life course perspective emphasizes understanding the dynamic interplay of personal, social, organizational, and historical factors and how this dynamism influences lives. I use a mixed method research design allowing me to analyze cross-sectional surveys (n=678, 23 NHs) and interview data (n=25, 2 NHs) complementing each other at various phases of this dissertation. I use multiple linear and ordinal logit regressions to estimate predictive models consisting of demographic, job quality, and life course factors influencing job satisfaction and employment intentions. I use modified grounded theory and constant comparative methods to interpretively identify themes in the interview data, leading to 20 major themes. Life course-relevant findings include intergroup differences in employment intentions by life course markers and identities, e.g., gender, race, marital status, life course stage, and career stage, demonstrating a significant impact on job satisfaction and intent to stay. Life course contingencies such as benefits, and breadwinner status also significantly influence job satisfaction and intent to stay. Further, I find evidence that cumulative disadvantages, job attitude, professional learning, equality and fairness, supportive leadership, emotional burnout and care concerns have additional significant impacts on both job satisfaction and intent to stay. Findings from the analysis of interviews lend support to the quantitative findings. This study has practical implications for nursing home leadership and policy makers. Nursing home leaders must amend their conventional strategies of retaining the LTC workforce by creating tailored support for workers while addressing job quality deficits. Access to mental health services, assessment of turnover risks at various life courses and career stages, professional support, increased leave for caregiving and respite, improved access to high-quality health insurance and benefits that assist this workforce in dealing with contingency factors and career mobility (e.g., small grants, tuition remission) have the potential to alleviate some of the instability in this often-vulnerable workforce.

Georgia State University Repository
Gender equality
Openalex Percentile: Top 38%
Geriatric Care and Nursing Homes
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.