Health informatics programs for continuing education for health executives in Western Africa, and delivery using distance on-line education: a systematic review
Bibliographic record
Abstract
Background Health informatics is defined as “the intersection of clinical, IM/IT and management practices to achieve better health” (COACH)1. In the developed world, the implementation of health informatics, with the goal of improving patient care and reducing costs, has been implemented in public health, hospitals, and physician practices. Accompanying these efforts are educational programs at the post-secondary, Bachelor, Masters and PhD level. The aim of these programs is to give graduates the skills necessary to continue the drive towards innovative health information technologies. These achievements have been documented in the health care literature. In developing countries, the lack of structured health informatics education programs is a barrier to addressing the need for these programs. Objective To systematically review the literature to determine: (1) what comprises a health informatics continuing education curriculum for health executives, (2) the applicability of said curriculum to Western African countries, and (3) the effectiveness of distance on-line education methods for delivering the curriculum. Methods Medline and Embase were searched over the time period January 2000 to February 2014. Google was also used to discover additional articles pertinent to the research objectives. Qualitative and quantitative studies were included if they focused on the use of distance on-line delivery for education and/or health informatics education, or discussed health informatics curricula for clinicians and non-clinicians, or addressed health informatics education in developing countries, or more than these three. Results 16 studies (7 quantitative, 9 qualitative) met the inclusion criteria. Educational needs for health executives were considered. In developing countries, Information and Communication Technology (ICT) infrastructure must have sufficient capacity to support delivery of educational materials and activities in order to be effective. A majority of the quantitative studies (6 out of 7) found that distance on-line delivery of education and associated technologies (podcasts, virtual environments) had positive effects on student engagement, learning and retention. Conclusions For busy health executives, short courses on health informatics can offer on-line assistance in providing continuing education. Distance on-line education is now an accepted approach to earning academic credentials and acquiring knowledge. Collaborative efforts between universities in developed and developing countries, along with grant funding, can greatly aid in delivering health informatics education to developing countries.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".