Determinants of the managerial staff’s disposition towards e-payment platforms in public tertiary hospitals in Enugu, Nigeria: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Many Nigerians pay out-of-pocket for their health care, and some hospitals have started utilising e-payment systems to increase transactional efficiency. The study investigated the type and usage of e-payment platforms in public hospitals and the factors that may influence the managerial staff's disposition towards using the e-payment system. METHODS: We conducted a cross-sectional survey of 300 managerial staff within the four public tertiary hospitals in Enugu, Nigeria, through proportionate quota sampling. The survey obtained participants' demographic characteristics, types of e-payment platforms, managerial staff's technophobia, perception of credibility, and disposition towards e-payment. Data were analysed using descriptive statistics, Spearman correlation, and hierarchical linear regression. RESULTS: The majority of the respondents (n = 278, 92.7% completion rate) aged 43.4 ± 7.6 years were females (59.0%) with a bachelor's degree (54.7%). Their disposition (80.0%±17.9%), perceptions of the usefulness (85.7 ± 13.9%), and user-friendliness (80.5 ± 18.1%) of e-payment in the hospital were positive, credibility (72.6 ± 20.1%) and technophobia (68.0 ± 20.7%) were moderate. There was a negative correlation between technophobia and disposition toward the use of e-payment (ρ = -0.50, P < 0.001). Significant multivariate predictors of managerial disposition towards e-payment were; being a woman (β = 0.12, P = 0.033), married (β = 0.18, P = 0.003), positive perception of usefulness (β = 0.14, P = 0.025), and credibility (β = 0.15, P = 0.032). CONCLUSION: Most participants had a positive disposition towards e-payment in public hospitals. However, managers with technophobia, a negative perception of e-payment usefulness, and credibility had a lesser disposition to its use. To ensure the universal implementation of e-payment in Nigerian hospitals, the service providers should make the e-payment platforms more secure and user-friendly to health services consumers and providers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".