Knowing, Caring, and Telehealth Technology
Bibliographic record
Abstract
The use of technology in delivery of health care services is rapidly increasing, and more nurses are using telehealth to provide care by distance to persons with complex health challenges. The rapid uptake of telehealth modalities and dynamic evolution of technologies has outpaced the generation of empirical knowledge to support nursing practice in this emerging field, specifically in relation to how nurses come to know the person and engage in holistic care in a virtual environment. Knowing the person and nursing care have historically been associated with physical presence and close proximity in the nurse-client relationship, and the use of telehealth can limit the ways in which a nurse can observe the person, potentiate perceptions of distance, and lead to a reductionist perspective in care. The purpose of this article is to illuminate the dynamic and evolving nature of nursing practice in relation to the use of telehealth and to highlight gaps in nursing knowledge specific to knowing the person in a virtual environment. Such an understanding is necessary to inform future research and generate empirical evidence to support nurses in providing ethical, safe, effective, and holistic care by distance to persons through telehealth technology.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".