Bibliographic record
Abstract
This paper will identify professional liability risks including licensure and malpractice risks associated with the delivery of professional health care services by nurses through the medium of information and communications technology (ICT). Risk management strategies to address these risks will be presented. In Canada, telehealth is defined as "the use of communications and information technology to deliver health and healthcare services and information over large and small distances" (Industry Canada, 1997). The use of ICT to deliver professional health care services in Canada is growing rapidly. New health care call centres, staffed by registered nurses, have burgeoned in the past 2 years and other health care organizations are now offering similar services. Whereas, in the "good old days", all health care providers were cautioned against giving health care information over the telephone because of the risk of error and possible malpractice suits, this is now an accepted practice. It is important to recognize that the legal risks are as high as they ever were unless they are appropriately managed through a variety of risk management strategies. Self-regulating groups of Canadian health professionals are also struggling with the legal ramifications of telehealth in relation to the locus of accountability of the health professional, when the client lives in a different jurisdiction from the nurse. This presentation will stress the importance of risk management in telehealth delivery. It is vitally important for the protection of the public and of health care professionals that telehealth services are delivered in a way that minimizes the risk of harm and subsequent legal action.
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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 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".