The individual and contextual determinants of the use of telemedicine: A descriptive study of the perceptions of Senegal's physicians and telemedicine projects managers
Bibliographic record
Abstract
Telemedicine is considered to be an effective strategy to aid in the recruitment and retention of physicians in underserved areas and, in doing so, improve access to healthcare. Telemedicine's use, however, depends on individual and contextual factors. Using a mixed methods design, we studied these factors in Senegal based on a micro, meso and macro framework. A quantitative questionnaire administered to 165 physicians working in public hospitals and 151 physicians working in district health centres was used to identify individual (micro) factors. This was augmented with qualitative descriptive data involving individual interviews with 30 physicians working in public hospitals, 36 physicians working in district health centres and 10 telemedicine project managers to identify contextual (meso and macro) factors. Physicians were selected using purposeful random sampling; managers through snowball sampling. Quantitative data were analyzed descriptively using SPSS 23 and qualitative data thematically using NVivo 10. At the micro level, we found that 72.1% of the physicians working in public hospitals and 82.1% of the physicians working in district health centres were likely to use telemedicine in their professional activities. At the meso level, we identified several technical, organizational and ethical factors, while at the macro level the study revealed a number of financial, political, legal, socioeconomic and cultural factors. We conclude that better awareness of the interplay between factors can assist health authorities to develop telemedicine in ways that will attract use by physicians, thus improving 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".