Employee engagement and its relation to hospital performance in a tertiary care teaching hospital
Bibliographic record
Abstract
Introduction: Employees in modern organizations are expected to be proactive, show initiative, and take responsibility for their own professional development and to be committed to high quality performance standards. Human resource in healthcare is one of the key components which is scarcely available and needs to be utilized in an efficient way. Assessment of employee engagement and identifying the key areas for work place improvement are important to be able to deliver timely and quality care.Methods: A cross-sectional survey was conducted at the Nizam’s Institute of Medical Sciences, India, in the year 2012-13, that included all the permanent employees of the institute except class IV employees. The required sample size was calculated as 286. A semi structured socio-demographic proforma was designed. Employee Experience Survey used in the current study is based on Ontario Hospital Association’s Quality Healthcare Workplace Model. Engagement scores were categorized into low, medium and high categories. Correlation analysis was done to identify the association between engagement score and both the outcomes: patient centered work environment (PCWE) and patient safety culture (PSC).Results: The mean employee engagement score in the study hospital was 20.98 (SD = 3.15) which falls in the medium level. 40.6% of employees were in low engagement group, 39.9% were in medium engagement group and 19.5% were in high engagement group. The mean PCWE score was 22.78 (SD = 3.19), and mean PSC was 21.62 (SD = 3.99) and there was a positive and significant association between employee engagement vs. PWCE and employee engagement vs. PSC.Conclusion: The study shows a significant association between employee engagement and the key hospital outcomes, PCWE and PSC. A further analysis of the engagement survey data is required to test the direction of association and to determine the change in their relationships with respect to time. National benchmarks for such outcome measures need to be developed to enable the hospitals nationwide to assess and compare the work environments.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".