Bibliographic record
Abstract
A recent PricewaterhouseCoopers survey (HealthInsiders, Fall/Winter 2000) found that fewer than one-third (29.0%) of respondents knew that videoconferencing could be used to conduct medical tests and make diagnoses. The survey, in which a representative sample of 2592 Canadians aged 15 and older were quizzed about their knowledge of and attitudes toward telemedicine, also found that only 26.1% of respondents were familiar with the concept of dispensing medical advice and assessments by videoconferencing. Nearly one-quarter (24.2%) of respondents had heard of robotic surgery, while 18.2% were familiar with the practice of disease monitoring by telephone or the Internet. However, only 12.3% of respondents had heard of teletriage, in which a health care professional advises a person whether or not to seek emergency or other medical care. Canadians' actual experience with telemedicine is very limited. For instance, teletriage is the most frequently encountered type of service, but only 2.7% of respondents had experienced it. Women and people with children under 16 were more likely to have used this type of service. Although almost half of respondents (43.2%) indicated that they would be somewhat or very comfortable receiving medical advice and being diagnosed by a doctor who is not in the same room, only 27.1% were comfortable with the possibility of undergoing a medical procedure via telemedicine technology. When asked to name the most important benefit of telemedicine, 30.8% cited international cooperation, with doctors in different countries exchanging support and advice. Almost one-quarter (23.8%) thought the most important benefit was the possibility of more equal access to health care services for all Canadians. The greatest concern about telemedicine involved responsibility and liability for malpractice and errors, with 87.8% of respondents indicating that they were very or somewhat concerned. Almost three-quarters (72.1%) said they were very or somewhat concerned about differences in rules and regulations for the provision of health care among countries or provinces.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.007 |
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".