Interrelationship among Personal Characteristics, Perceptions, and Self-Efficacy on Electronic Medical Record System (ERNRS) Use among Health Professionals
Bibliographic record
Abstract
Improvements in the quality and safety of patient treatment are enhanced with the use of electronic medical records (EMRs). Despite the use of EMR, no established data existed on perceptions and self-efficacy and their relationship at the local level. The study assessed the interrelationships among personal characteristics, perceptions, and self-efficacy on EMR system use among 306 health professionals of a tertiary private hospital in Pasig, Metro Manila, Philippines, for the second quarter of 2023 who were chosen utilizing a proportionate stratified random sampling. This quantitative research used the descriptive, correlational design. Findings revealed that most respondents were young adults, females, had bachelor's degrees, had good typing ability, and had training in EMR systems. Most belonged to the medical department, used the system moderately, and served for 1-3 years. Overall, perceptions of EMR and self-efficacy were good. All the personal characteristics had a relationship with perceptions of EMR. All personal characteristics, except gender, were correlated with self-efficacy. However, gender was not. Lastly, perceptions of EMR had a relationship with self-efficacy. To address the findings, an action plan for telehealth utilization was created. In conclusion, perceptions of EMR and self-efficacy are influenced by personal characteristics, while perceptions of EMR influence self-efficacy.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".