Bibliographic record
Abstract
Tele-education has been used for many years to deliver continuing education programmes to rural health-care professionals. The main modes are audio, video and computer. Audio technologies involve the transmission of the spoken word (voice) between learners and instructors, either synchronously or asynchronously. Examples of the former include audioconferencing and short-wave radio; examples of the latter include audiotape or audiocassette. Video for distance learning, like audio, can be used in either synchronous or asynchronous fashion. Videoconferencing, or interactive television, are considered synchronous because there is the opportunity for live visual and verbal interaction between instructors and learners. Asynchronous instructional video tools include slow-scan video, interactive videodiscs and videotapes. Computer-assisted learning or instruction can be defined as any learning that is mediated by a computer and which requires no direct interaction between the user and a human instructor in order to run. It is becoming increasingly common. Examples include: the Internet and World Wide Web, email, synchronous and asynchronous computer-mediated communication applications and interactive multimedia applications on CD-ROM. Tele-education technologies have an important role to play in addressing the professional isolation which is experienced by rural and remote health-care professionals.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.292 | 0.091 |
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".