Factors Affecting Job Satisfaction among the Biomedical Equipment Technicians Working in Health Sector of Nepal: A Mixed-Methods Study
Bibliographic record
Abstract
Background: Job satisfaction is a vital concern for both managers and academics. Biomedical Equipment Technicians (BMETs) exemplify technical professionals and have also been affected by the factors of job satisfaction. This research is mainly focused on the job satisfaction of BMETs working in government and private health facilities to determine the factors affecting job satisfaction in their present scenario. Methods: We distributed questionnaires to all 148 Biomedical Equipment Technicians (BMETs) in Nepal, with 91 of them responding. Additionally, we gathered qualitative data from 6 BMETs from 6 health facilities, evenly split between urban and non-urban regions, with 3 facilities from each type. 18 interviews (FGD with the department in charges, interview with the Medical Superintendent and an interview BMET of each facility) were also conducted across these 6 health facilities. Our analysis included a descriptive examination of the survey data and a comparison of job satisfaction factors between the government and private sectors. Furthermore, we utilized NVivo 12 to code the qualitative data based on themes. Results: The data shows that there is no significant difference between Biomedical Equipment Technicians (BMETs) in the government and private sectors overall. However, it does reveal that BMETs in government hospitals tend to be more satisfied with their earnings compared to those in the private sector. While the overall levels of satisfaction and dissatisfaction are similar for both groups of participants, some of the specific levels of satisfaction and dissatisfaction differ between them. Conclusion: Overall, BMETs find enjoyment in their work, showcasing their dedication to their roles despite the various unsatisfactory factors present in hospital settings in both government and private sectors.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".