Front line workers in long-term care: the effect of educational interventions and stabilization of staffing ratios on turnover and absenteeism.
Bibliographic record
Abstract
OBJECTIVE: Front line workers in long-term care play a crucial role in helping residents achieve their highest possible functional level. High turnover (rates in excess of 100% are common) and absenteeism threaten the ability of long-term care providers to meet this goal. The purpose of this study was to determine the effect of a formalized certified nursing assistant (CNA) education program and stabilization of staffing ratios on turnover and absenteeism. DESIGN AND SETTING: This study was a 12-month prospective, nonrandomized trial involving two long-term care facilities providing traditional intermediate and skilled care serving as study sites and a similar facility serving as a control. For historical comparisons, each facility served as its own control using data from the year before the interventions. INTERVENTIONS: An in-house educational program based on the State of North Carolina core curriculum for CNAs was instituted in each study site. During the study period, efforts were made to achieve stable staffing ratios of 1 CNA:8 residents for days, 1:10 for evenings, and 1:15 for nights. Traditional quality of care indicators and resident/surrogate satisfaction were tracked during the study period. RESULTS: Both study facilities showed a decline in turnover, with the decline reaching statistically significant levels at Facility B (134% to 41%, P = 0.0001). Absenteeism rates did not change significantly during the study period. Resident/surrogate satisfaction with nursing care was improved significantly at Facility B (P = 0.02). CONCLUSION: A formal education program in conjunction with stabilization of staffing ratios can result in lower turnover rates for CNA's and improved resident/surrogate satisfaction.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".