Analysis of Productivity Improvement Act for Clinical Staff Working in the Health System: A Qualitative Study
Bibliographic record
Abstract
INTRODUCTION: The productivity of healthcare staff is one of the main issues for health managers. This study explores the concept of executive regulation of Productivity Improvement Act of clinical staff in health. METHODS: In this study phenomenological methodology has been employed. The data were collected through semi-structured interviews and focus group composed of 10 hospital experts and experts in human resources department working in headquarter of Mashhad University of Medical Sciences and 16 nursing managers working in public and private hospitals of Mashhad using purposive sampling. Findings were analyzed using Colaizzi's seven step method. RESULTS: The strengths of this Act included increasing spirit of hope in nurses, paying attention to quality of nursing care and decreasing problems related to the work plan development. Some of the weaknesses of Productivity Improvement Act included lack of required executive mechanisms, lack of considering nursing productivity indicator, increasing non-public hospitals problems, discrimination between employees, and removal of resting on night shifts. Suggestions were introduced to strengthen the Act such as increased organizational posts, use of a coefficient for wage in unusual work shifts and consideration of a performance indicator. CONCLUSION: The results may be used as a proper tool for long term management planning at organization level. Finally, if high quality care by health system staff is expected, in the first step, we should take care of them through proper policy making and focusing on occupational characteristics of the target group so that it does not result in discrimination among the staff.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.182 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".