Picturing the Nurse-Person/Family/Community Process in the Year 2050
Bibliographic record
Abstract
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.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.015 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".