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Record W2137720820 · doi:10.1177/0894318406296283

Picturing the Nurse-Person/Family/Community Process in the Year 2050

2007· article· en· W2137720820 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.

Bibliographic record

VenueNursing Science Quarterly · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicInformation Society and Technology Trends
Canadian institutionsYork University
Fundersnot available
KeywordsReverenceCompassionNursing processNursingProcess (computing)HumanityAdaptation (eye)Quality (philosophy)PsychologyNursing theoryMedicineMEDLINEComputer scienceEpistemology

Abstract

fetched live from OpenAlex

How will nurses relate with persons in the year 2050? And, how might technology enable or limit the nursing process with persons, families, and communities? These are the questions addressed in this column. Imaging practice in light of the technological imaginings and projections is facilitated by a possible scenario that includes robotics that not only monitor human biological processes, they also emote compassion and caring that may one day be dosed according to the latest diagnostic prescription. Three nurses in this column present their views of how nursing might evolve. Karnick, aligned with the human becoming school of thought, imagines a practice anchored in respect for humanity and quality of life and an accompanying respect for nursing knowledge and nursing work. Senesac and Sato, aligned with Roy's adaptation model, call for nurses to envision and choose the future they want to have. Clear in both perspectives is a reverence for human values and human experience and for the critical role of nursing knowledge as we move toward the not-yet of 2050.

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)0.007
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0040.004
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.362
Teacher spread0.331 · 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