Personhood: An evolutionary concept analysis for nursing ethics, theory, practice, and research
Bibliographic record
Abstract
BACKGROUND: The purpose of this concept analysis was to examine how the concept of personhood has been used in the nursing literature. The person is central to nursing, as the object of nursing work, or care, and a key element of theory. Health and illness confront conventional notions of personhood based on the Western philosophy, in delineating boundaries of life and death, and grappling with pathophysiological changes and alterations in capacities that challenge our understandings of what makes a person whole. METHODS: Rodgers' evolutionary method was selected; it emphasizes the relationship between concepts, language, and communities of users. A literature search between 1950 and 2017 generated 760 articles; 54 were retained for analysis. RESULTS: Four themes were identified: (1) personhood and nursing ethics, emphasizing scientific advances, and establishing criteria; (2) personhood as a morally significant, relational process realized through nursing care; (3) personhood lost (or neglected); (4) interventions aimed at understanding, recognizing, and enhancing personhood. Related terms, antecedent concepts, and consequences are explored. CONCLUSIONS: This preliminary view of personhood in the nursing literature demonstrated how the concept has been developed, used, and understood. Areas for future research include nursing ethics, theory, and clinical practice, as well as links with other academic disciplines.
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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 | 0.033 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".