Getting a Picture
Bibliographic record
Abstract
Delivery of care by nurses in virtual environments is rapidly increasing with uptake of digitally mediated technologies, such as remote patient monitoring (RPM). Knowing the person is a phenomenon in nursing practice deemed requisite to building relationships and informing clinical decisions, but it has not been studied in virtual environments. PURPOSE OF STUDY: The intent of this study was to explicate the processes of how nurses come to know the person using RPM, one form of telehealth technology used in a virtual environment. STUDY DESIGN AND METHOD: The study was informed by Charmaz's constructivist grounded theory and included 33 interviews and 5 observational experiences of nurses using RPM in 7 different settings. FINDINGS: Getting a Picture evolved as the core category to a theoretical conceptualization of nurses knowing the person through use of RPM and other technologies, such as telephone and electronic medical records. Getting a Picture reflected a dynamic flow and integration of seven processes, such as Connecting With the Person and Recording and Reflecting, to describe how nurses strove to attain a visualization of the person. CONCLUSIONS: While navigating disparate and disconnected information and communication technologies, Getting a Picture was important for providing safe, holistic, person-centered care.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 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".