The first year: employment patterns and job perceptions of nursing assistants in a rural setting
Bibliographic record
Abstract
AIM: The aim of this study was to follow rural certified nursing assistants (CNAs) (n=123) in the United States for 1 year post-training to identify retention and turnover issues in the long-term care (LTC) setting by exploring the CNAs' perceptions of the LTC work experience. BACKGROUND: Turnover among CNAs impacts the quality of care, imposes a financial burden on facilities and taxpayers, and creates increased stress and workloads on those who remain. METHOD: A longitudinal survey design was used to track individuals completing CNA training for 1 year. RESULTS: At 1 year post-training, 53.7% of respondents currently worked in LTC, 30.9% worked in LTC and left, and the remaining 15.4% never worked in LTC. CONCLUSION: While the training site does not appear to impact retention, the first 6 months of employment appear critical. The CNAs cited pay as a reason for leaving LTC, but better pay did not characterize the jobs taken by the CNAs who left. Implications for nursing management. This study highlights the importance of the first 6 months of employment to retention and provides practical information for nurse managers evaluating the resource-effectiveness of hosting training programmes. Additionally, the key issues influencing retention were identified and practical suggestions for nurse managers to improve retention are provided.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".