Assessment of job satisfaction and contributing factors among community health officers in Chhattisgarh, India
Bibliographic record
Abstract
Background: Job satisfaction plays a crucial role in the performance, motivation, and retention of healthcare professionals. Community health officers (CHOs), introduced under the Ayushman Bharat initiative, are pivotal in delivering primary healthcare services at health and wellness centers (HWCs). This study assesses the job satisfaction of CHOs working in the HWCs-sub-health centers (HWC-SHCs) of Chhattisgarh, exploring the key factors affecting their motivation and retention. Methods: A mixed-methods approach was employed. The quantitative component included 100 CHOs from Raipur (non-tribal) and Bijapur (tribal) districts, using a five-point Likert scale for job satisfaction assessment. For the qualitative component, 16 in-depth interviews were conducted to explore personal experiences, challenges, and motivating factors. Data were analyzed using SPSS v.22 and thematic analysis. Results: A total of 61% of CHOs reported dissatisfaction with their jobs, with the most dissatisfaction in monetary benefits (89%). Factors like workload, lack of safety, and management challenges contributed to dissatisfaction. However, CHOs were most satisfied with self-appraisal (80%) and their relationships with coworkers (74%). Bijapur district reported higher satisfaction (56.5%) compared to Raipur (33.8%). Conclusions: The study highlights the need for improved salaries, better safety measures, and workload reduction to enhance CHO job satisfaction. Policy changes addressing these issues are essential for sustainable healthcare delivery in both tribal and non-tribal areas.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| 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".