Assessment of health provider readiness for telemedicine services in Uganda
Bibliographic record
Abstract
BACKGROUND:: There are few telemedicine projects in Africa that have reached scale. One of the reasons proposed for this has been failure to assess health provider readiness for telemedicine prior to implementation. OBJECTIVE:: To assess health provider readiness for implementation and integration of telemedicine services at three levels of Uganda's health facilities, namely, a national referral hospital (NRH), regional referral hospitals (RRHs) and level 4 health centres (HC-IVs) and to investigate factors associated with readiness for telemedicine. METHOD:: A cross-sectional descriptive study was conducted at public healthcare facilities in Uganda. One RRH and HC-IV was identified from each of the Western, Eastern and Northern regions using a multistage random sampling technique. Mulago Hospital, which doubles as an RRH and HC-IV in the central region, was purposively identified for the study. After validation, a questionnaire was distributed for self-administration to senior administrators and doctors selected at the NRH, RRHs and HC-IVs. Data were analysed using bivariate associations between the outcome and the potential independent variables. RESULTS:: In total, 114 healthcare workers completed the questionnaire. Of the respondents, 24 (21%) were from HC-IVs, 44 (39%) were from RRHs, and 46 (40%) from NRH. Doctors made up 45.8% (11) of respondents at HC-IVs, 59% (26) at RRHs, and 30.4% (14) at NRH. Administrators across all health facility levels were more likely to integrate telemedicine into the healthcare system than doctors (odd ratio = 1.39 [95% confidence interval = 0.38-4.95]). A significant association existed between the state of readiness and type of health facility, p < 0.001. The NRH and RRHs are more likely to integrate telemedicine into their systems than the HC-IVs. Among the factors investigated (job title, health facility, technology type, reason for referral and frequency of electronic communication), the level of health facility and title or role of healthcare worker were found to have a significant statistical association with being ready to integrate telemedicine into the healthcare system. CONCLUSION:: Health provider readiness to integrate telemedicine services varies at the different levels of the health facility and job title or role. However, referral hospitals and administrators were more likely to integrate telemedicine than HC-IVs and doctors, respectively. While this study shows physicians and administrators are ready, other sectors (nurses, allied healthcare workers, public) will also need to be assessed.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".