Improve employee engagement to retain your workforce
Bibliographic record
Abstract
BACKGROUND: Turnover hurts patient care quality and is expensive to hospitals. Improved employee engagement could encourage employees to stay at their organization. PURPOSE: The aim of the study was to test whether participants in an employee engagement program were less likely than nonparticipants to leave their job. METHODS: Health care workers (primarily patient care technicians and assistants, n = 216) were recruited to participate in an engagement program that helps employees find meaning and connection in their work. Using human resources data, we created a longitudinal study to compare participating versus nonparticipating employees in the same job titles on retention time (i.e., termination risk). FINDINGS: Participants were less likely to leave the hospital compared to nonparticipating employees (hazard ratio = 0.22, 95% CI [0.11, 0.84]). This finding remained significant after adjusting for covariates (hazard ratio = 0.37, 95% CI [0.17, 0.57]). PRACTICE IMPLICATIONS: Improving employee engagement resulted in employees staying longer at the hospital.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".