An exploratory study of job satisfaction of general practitioners in the Yangon region, Myanmar
Bibliographic record
Abstract
literature Search, G -Funds CollectionBackground.General practice in Myanmar will develop in the coming few years due to its involvement in the National Health Plan (2017-2021) [1].General practitioners' job satisfaction is an essential factor in the quality of health care.Objectives.The objective of our study is to evaluate the job satisfaction of general practitioners in the Yangon region.Material and methods.This cross-sectional descriptive study was conducted on general practitioners in the Yangon region using the 10-element Warr-Cook-Wall Job Satisfaction Scale.Statistical analysis and testing were performed by descriptive analysis, correlation analyses, stepwise linear regression, independent student t-Test and ANOVA analysis.Results.As participants, 257 general practitioners in the Yangon region were included in this study.The majority of the participants were 25 to 35 years of age and had less than 5 years of experience.The overall ratings of job satisfaction of the participants were quite high.In the study, general practitioners are satisfied with "Freedom of working method" and "Amount of responsibility", with the highest mean being 5.54 and 5.44, respectively.Contradictorily, they were dissatisfied with "Income" (mean = 4.77) and "Colleagues and fellow workers" (mean = 4.91).The "Opportunity to use abilities" was a high predictor of overall job satisfaction in linear regression analysis.Conclusions.In conclusion, this study provides elements that help GPs be satisfied with their work.If the elements highlighted in this study are emphasised and supported, the primary care and health care system of Myanmar will strengthen and develop.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".