Electronic Medical Record Attitudes and Predictors of Adoption Among Ethiopian Health Professionals: Cross-Sectional Study
Bibliographic record
Abstract
Background: Electronic medical records (EMRs) are increasingly adopted globally to improve health care delivery, yet challenges remain in their acceptance, defined here as favorable attitudes toward their use among health professionals. Understanding factors influencing acceptance is critical for successful implementation. Objective: This study aimed to identify predictors (or factors) associated with favorable attitudes toward EMRs among health professionals in 3 Ethiopian hospitals. Methods: A cross-sectional study was conducted from January to March 2025 in 3 Ethiopian hospitals implementing EMRs. A systematic random sampling method was used to initially select 397 health professionals, and data were collected using a structured questionnaire. Multivariate logistic regression was employed to identify predictors of favorable attitudes toward EMRs. Results: Of the final 382 professionals, 198 (51.8%, 95% CI 0.43-0.53) showed favorable attitudes. Predictors of positive attitude included computer literacy (adjusted odds ratio [AOR] 2.66, 95% CI 1.16-6.09; P=.02), EMR training (AOR 2.87, 95% CI 1.80-4.56; P<.001), and age of 29 years or younger (AOR 3.05, 95% CI 1.58-5.9; P=.001). Conclusions: Improving computer literacy, providing refresher training, and strengthening management support are key strategies for enhancing health professionals' attitudes toward EMRs. Future research should explore qualitative insights into barriers and facilitators of EMR adoption.
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 | 0.004 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".