An Exploration of the Role of Hospital Committees to Enhance Productivity
Bibliographic record
Abstract
Productivity is the main concern of hospitals as organizations providing health services. As the role of hospital committees is increasing and their productivity and performance improvement is very important, the present study was performed to identify weaknesses and strengths of committee sessions. This analytical-descriptive study was conducted cross- sectional from January to April in 2012. Summary of 405 committee session's agendas related to 11 kinds of committees in 8 hospitals (out of 23 hospitals) of capital cities in 3 provinces of Sistan and Balouchestan, South Khorasan and Khorasan Razavi in Iran were extracted. Data was collected through a form and was analyzed by SPSS16 software using descriptive statistics and variance analysis and content analysis technique. This study showed that the number of hospital committee's sessions holding in 2012 was more than 2011.The differences between public and private hospitals in terms of the following subjects were significant (P-Value < 0.001). In terms of the number of selected policies, participants of the committees, and the duration of the sessions the public hospitals had better conditions. And regarding documentation process, feedback of decisions to personnel and the implementation of the formulated policies in the committees, private hospitals performed better. According to the results of this study, to improve the productivity of hospital committees, it is suggested to motivate senior, tactical and operational managers to appropriately participate in the committees and necessary planning for the committees in advance is mandatory.
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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 | 0.011 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".