Telemedicine options to address identified health needs in Botswana
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| 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 | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".