Physicians’ perceptions on the impact of telemedicine on recruitment and retention in underserved areas: a descriptive study in Senegal
Bibliographic record
Abstract
BACKGROUND: Similar to many places, physicians in Senegal are unevenly distributed. Telemedicine is considered a potential solution to this problem. This study investigated the perceptions of Senegal's physicians of the impact of telemedicine on their recruitment to and retention in underserved areas. METHODS: We conducted individual interviews with a random sample of 60 physicians in Senegal, including 30 physicians working in public hospitals and 30 physicians working in district health centres between January and June 2014, as part of a mixed methods study. Data were collected using a semi-structured interview guide comprising both open- and close-ended questions. Interviews were recorded, transcribed and coded thematically using NVivo 10 software using a priori and emergent codes. Participants' characteristics were analyzed descriptively using SPSS 23. RESULTS: The impact of telemedicine on physicians' recruitment and retention in underserved areas was perceived with some variability. Among the physicians who were interviewed, most (36) thought that telemedicine could have a positive impact on their recruitment and retention but many (24) believed the opposite. The advantages noted by the first included telemedicine's ability to break their professional isolation and reduce the stress related to this, facilitate their distance learning and improve their working conditions. They did acknowledge that it is not sufficient in itself, an opinion also shared by physicians who did not believe that telemedicine could affect their recruitment and retention. Both identified contextual, economic, educational, family, individual, organizational and professional factors as influential. CONCLUSION: Based on these opinions of physicians, telemedicine promotion is one intervention that, alongside others, could be promoted to assist in addressing the multiple factors that influence physicians' recruitment and retention in underserved areas.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".