Impact of Digital Literacy Levels of Health Care Professionals on Perceived Quality of Care
Bibliographic record
Abstract
Background Multiple digital technologies were used during and after the COVID-19 pandemic with an intent to improve quality of patient care. It has been seen that the perception of patients toward the use of digital solutions in clinical care varies significantly. This has also been attributed to varying levels of digital literacy among the health care professionals (HCP) involved in patient care. Objective Our paper aims to study the impact of digital literacy levels of HCPs, including hospital attendants and support staff who were involved in a clinical care team of COVID-19 patients, so that barriers toward the implementation of digital health solutions could be identified. Methods A standardized survey using responses based on Likert scale was developed, which measured the confidence levels of HCPs and their attitudes toward digital technologies. The survey consisted of questions from the Technology Acceptance Model as well as the unified theory of acceptancy and use of technology to assess the attitude of HCPs. A total of 100 Hospital attendants directly employed in patient care were enrolled in the study. They were also asked to respond to feedback received from patients on the perceived quality of care. Results Around 60% of the HCPs showed high digital literacy levels. Most respondents showed confidence in the use of technology. Moreover, around 20% of HCPs showed apprehension toward using digital solutions for direct patient care. A significant difference was found between study population with high digital literacy and perceived quality of care. Conclusions Our study found that poor digital literacy in HCPs adversely affects the safety and quality of patient care. It is important that institutions provide targeted education and training to not only doctors and nursing staff but also other support staff with low digital literacy levels and to boost their confidence in providing clinical care.
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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 | 0.003 | 0.034 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".