Telemedicine options to address identified health needs in Botswana
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Objective: Global efforts to implement national ehealth strategies have occurred, yet specific telemedicine implementations have fallen behind. A weakness inherent within many, perhaps most, national ehealth strategies, including Botswana's - is a lack of telemedicine focus. This is despite its potential to address many current healthcare system needs. The development of a telemedicine-specific strategy, to complement the existing ehealth strategy, has been proposed. This paper reports on an emulated process to determine prioritised health needs, identify broad solutions, consider ehealth and then telemedicine solutions, and prioritise these as insight for telemedicine-specific strategy development. Methods: The eHealth Strategy Development Framework (eHSDF) was adopted and steps 5-7 were emulated. Key informants participated in telephone-based semi-structured interviews in November 2020, using a key informant interview guide. Participants were asked specific questions related to national health needs, proposed solutions, and prioritisation. The interviews were recorded and transcribed for analysis. Results: Eleven key informants identified the top five perceived health issues as human resource shortages, congestion and overcrowding, prevalence of diseases, poor referral system, and lack of diagnostic and case management skills. Solutions were proposed, some of which included: Telehealth (including telemedicine), health informatics, and elearning. Telemedicine solutions included: a health professional help desk, teleconsultations, and apps for specialist referral. eLearning solutions were training, mentoring, and continuing professional development. Conclusion: A telemedicine-specific strategy, addressing the identified health issues and aligned to the existing national ehealth strategy, would provide the required focus to enable the development and deployment of telemedicine activities in the country.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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 it