Meaningful recognition program for nursing faculty insights learned during the pandemic
Bibliographic record
Abstract
Nursing programs face faculty shortages further aggravated by burnout and low pay compared to the private sector. As meaningful recognition programs are linked to resiliency and improved job satisfaction, this university initially implemented the DAISY Award for Extraordinary Nursing Faculty program in 2014. With the significant changes experienced during the pandemic, the university wanted to strengthen the Daisy Award program and determine its impact on Compassion Satisfaction (CS) and Compassion Fatigue (CF). Nursing faculty are at increased risk for CF (burnout and secondary traumatic stress) due to clinical errors, patient illness, death, and multicultural differences. These risks have increased across nursing settings with the pandemic. In the clinical setting, research has shown that effective implementation of the Daisy Award Program provides nurses with meaningful recognition that increases CS and decreases CF. There is limited literature on how meaningful recognition programs influence CS and CF for nursing faculty. The purpose of this research study is to evaluate whether strategies to improve the DAISY Award program influence CS and CF for nursing faculty. The study design was quasi-experimental, utilizing a pre-and post-survey design following interventions to strengthen the DAISY Award program through centralized communication and recognition strategies. Across the two data collection periods, CS remained high and CF low (non-significant findings) overall, though visiting professors had statistically significantly higher CS and lower CF than full-time faculty. Given the pandemic timing, it is unknown if the meaningful recognition program contributed to maintaining the desired CS and CF results, and further research is needed.
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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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".