MétaCan
Menu
Back to cohort
Record W1971188305 · doi:10.1177/0898010112465357

Knowing, Caring, and Telehealth Technology

2012· article· en· W1971188305 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Holistic Nursing · 2012
Typearticle
Languageen
FieldMedicine
TopicEmpathy and Medical Education
Canadian institutionsMcGill University
FundersCanadian Institutes of Health Research
KeywordsTelehealthTelemedicinePsychologyNursingMedicineHealth carePolitical science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.039
GPT teacher head0.383
Teacher spread0.343 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it