Work engagement levels and correlates among physician assistants in Ghana: a cross-sectional study
Bibliographic record
Abstract
Background Work engagement (WE) is critical to quality primary healthcare delivery. However, limited research has explored its levels and determinants among healthcare professionals in low- and middle-income countries. This study assessed the levels and correlates of work engagement among physician assistants (PAs) in Ghana. Methods A cross-sectional study was conducted among 439 PAs from October to December 2024. Participants were recruited via emails, social media platforms, and posters featuring study links and scannable questionnaire codes. WE was measured using the validated Utrecht Work Engagement Scale questionnaire. Results Overall, WE levels were average, with similar trends across the three subdomains. In the bootstrapped multivariate linear regression model, anxiety was negatively associated with WE ( β = − 0.49, 95% confidence interval [ CI ]: −0.77 to −0.21). Conversely, working in an urban area ( β = 0.36, 95% CI : 0.05 to 0.67), holding the rank of PA/Senior PA ( β = 0.27, 95% CI : 0.03 to 0.52), reporting good self-rated health (β = 0.54, 95% CI : 0.19 to 0.88), and working at health centers ( β = 0.86, 95% CI : 0.22 to 1.50) were positively associated with WE levels. Conclusion WE levels are average in the study sample, highlighting the need for strategic interventions to improve and sustain the healthcare workforce’s motivation and performance. Addressing workplace stressors, enhancing professional development opportunities, and fostering supportive work environments could improve engagement among PAs and healthcare professionals in general. Strengthening WE is essential for ensuring resilient quality primary healthcare systems and achieving the goals of universal health coverage.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".