Bibliographic record
Abstract
As with many disciplines, the fields of healthcare in general and medicine, in particular, have made vast strides in improving patient outcomes and healthcare delivery. But, have healthcare professionals and medical academia been able to maximize the utilization of new technologies to improve the delivery of the right knowledge, to the right people, at the right time across geographical boundaries? In order to provide the best quality of care, regardless of patient or provider location, specific issues must be addressed. Healthcare consumers and providers recognize that the system is often over worked, time constrained, poorly funded and desperately in need of a means to maintain up-to-date knowledge and efficient skills in order to deliver the best quality of care (Health Canada, 1998). We also know that there is a large disparity in both the quality and types of healthcare available between developed and developing countries (Lown, Bukuchi & Xavier, 1998). Within a single country there are also differences in healthcare services based upon location (rural vs. urban areas), wealth, age, gender and a host of other factors (Health Canada, 2004). However, because Information and Communication Technologies (ICT) can be a simple and cost effective tool, it can make desperately needed medical knowledge available to developing coun tries (Pakenham-Walsh, Smith & Priestly, 1997). Furthermore, it is becoming more difficult to get physicians and extended healthcare professionals to participate in face-to-face seminars in order to learn about the progress and changes in the delivery of healthcare. Time, travel requirements and cost are the biggest barriers to overcome. For rural areas and developing countries these issues are even more evident (Ernst and Young, 1998). Today, many institutions and countries are exploring and implementing ICT solutions to help reduce these inequities. The fact remains however that in the case of developing countries, a critical shortage of healthcare professionals remains (Fraser and McGrath, 2000). Adding to the problem is the fact that the telecommunications network, the backbone of ICT, in Africa is the least developed in the world (Coeur de Roy, 1997) This article concentrates on two main aspects of ICT. First, it examines ways in which ICT can assist in information and knowledge transfer and second, it explores the challenges of ICT implementation. Request access from your librarian to read this chapter's full text.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.012 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".