Determinants of the Intention of Senegal's Physicians to Use Telemedicine in Their Professional Activities
Bibliographic record
Abstract
BACKGROUND: In Senegal, physicians are unevenly distributed, leading to unequal access to healthcare in underserved areas. Telemedicine is seen as a potential means to address this problem. INTRODUCTION: Telemedicine's potential to improve access depends, in part, on physicians' intention to use the technology. In Senegal that intention is not well known. This study aimed to determine that intention and the factors that influence it. MATERIALS AND METHODS: We conducted a cross-sectional study between January and February 2015 with a random sample of 168 physicians working in public hospitals and 153 in district health centers in Senegal. Data were collected using two questionnaires and analyzed using descriptive statistics, correlations, and linear regression. RESULTS: The intention to use telemedicine by physicians working in public hospitals and district health centers was moderate and was positively correlated with their attitude, subjective norm, and perceived behavioral control. The intention of the physicians working in public hospitals was also positively correlated with their region and status as contract employee, but negatively with their status as government employee. That of the physicians working in district health centers was negatively correlated with their age and years of practice. DISCUSSION: These results showed that, overall, the intention of Senegal's physicians to use telemedicine was moderate and could be improved by acting on factors related to their perceived behavioral control and other factors correlated with their intention. CONCLUSIONS: Physicians' intention to use telemedicine in Senegal is fair but could be improved by addressing the factors identified in this study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".