Improving performance by increasing job motivation among midwives in Babol University of Medical Sciences
Bibliographic record
Abstract
Background: It is important to understand midwiveschr('39') perceptions about their jobs and factors that influence their motivation. The aim of this study was to investigate and describe the main factors influencing job motivation among midwives at Babol University of Medical Sciences, Babol, Iran. Methods: This cross-sectional study was carried out on midwives at Babol University of Medical Sciences in 2012. A total of 44 midwifes were selected using a systemic random sampling method and sampling proportionate to size. A questionnaire comprising 26 questions was used to assess the main factors influencing job motivation. The main areas to be addressed were: functional job analysis, in service training and the objective use of performance assessment. We organized an in-service training committee, which provided training programmed based on the needs of midwives. Also, we did set up a performance appraisal committee in order to ensure an objective use of existing performance appraisal form and – after getting permission grant – we changed it based on job description. Results: The results of our informal questionnaire survey provided a comprehensive view of motivation among midwives in Babol University of Medical Sciences. Conclusion: Low motivation and dissatisfaction were widespread, and can be attributed to salary and remuneration, intensive job regulation, functional job description, in-service training, job opportunity, and performance appraisal mechanisms.
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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 | 0.002 | 0.004 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".