Improving the process of employee recognition: An exploratory study
Bibliographic record
Abstract
Background: Employee motivation, retention, replacement, and recruitment of human resources will be key strategic imperatives for health care organizations. Awards and recognition programs that build employee loyalty, overall experience and satisfaction are important requirements for any organization. We conducted a baseline analysis of employee recognition practice in a large hospital.Methods: An exploratory study was conducted using data collected from health care workers, regardless of the category employed. Using web-based survey designed in Google R forms. Questionnaire responses were downloaded, and then analyzed.Results: 331 completed online survey responses. Of these, 87% of the participants were females, 88% were from clinical disciplines, and 48% were working at the hospital for more than ten years. 65% of the respondents were frontline health care workers. 88% of participants indicated that it was meaningful to be appreciated. The employee net promoter score across the surveyed participants was 27% of the participants were categorized as promoters, whilst 47% were detractors. 26% were staff with more than 10 years’ experience had the highest employee promoter score, whilst non-clinical staff had the lowest (-51). Females had a lower net promoter score (-23) when compared to males (-2). Although on bivariate analysis of males (OR 1.42) and staff with a positive attitude (OR 1.09) were more likely to be promoters, these were not statistically significant. Clinical staff showed an increased likelihood of being detractors based on bivariate (OR 1.59) and multivariate analysis (OR 1.72), but were not statistically significant.Conclusions: The study showed a low employee and a secondary finding of a gender difference in the net promoter score, with females scoring less. Further qualitative studies are required to explain the contextual factors surrounding these differences and low promoter scores.
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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 | 0.002 | 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 | 0.001 |
| 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".