Job satisfaction and its associated factors among optometrists in Ghana: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Job satisfaction describes an employee's motivation and/or feeling of satisfaction towards his/her work. Globally, healthcare professionals' turnover and retention play a critical role in the delivery of essential health services. In Ghana, however, little has been done to ascertain job satisfaction levels among human resources for eye-health. The objective of this study therefore was to assess job satisfaction and its associated factors among optometrists in Ghana. METHODS: A cross-sectional survey was conducted among 304 registered and licensed optometrists of the Ghana Optometric Association between September 2018 and June 2019. A validated, well-structured questionnaire was used to elicit information on socio-demographic characteristics of participants and measures on job satisfaction. Scores from a five-point Likert scale was employed to examine job satisfaction and its associated factors. Linear regression analyses were used to evaluate the association between overall job satisfaction and its associated factors using Rasch logit scores. RESULTS: A total of 214 optometrists gave valid responses to the questionnaires used for the final analysis. The mean (± SD) score of the overall perception of job satisfaction among optometrists was 3.36 (± 1.00), with 74.3% of them being satisfied with their jobs. After statistical adjustment, Good work-life balance (Unstandardized co-efficient (β) = 0.288, p = 0.001), Salary (β = 0.222, p < 0.0005), Supervision (β = 0.117, p = 0.044), and Continuing Education Opportunities (β = 0.138, p = 0.017) were all significantly associated with higher levels of overall job satisfaction. CONCLUSIONS: Most optometrists were satisfied with their jobs. Effective strategic planning and management of human resources for eye-health in Ghana are essential in the development of quality eye-health systems and the provision of high-quality eyecare services.
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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 | 0.001 |
| 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 | 0.000 | 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".