Bibliographic record
Abstract
Nursing as a human science focuses on life and health as humanly experienced. Intentionality is a philosophical idea of particular significance to nursing as a human science, particularly within Parse’s theory of human becoming and Watson’s theory of human caring. These two theorists’ interpretations of intentionality are explored in relation to their respective theories and the implications for research and practice. A persistent thread in nursing discourse positions nursing as a human science—that is, one that focuses on life and health as humanly experienced. In human science disciplines, human beings are viewed as the subjects of their own lives, rather than as mere objects or things, as is the case in the natural sciences. My purpose in this column is to explore intentionality as a philosophical idea of particular significance to theoretical accounts of nursing as a human science. Before beginning, it is important to note that not all nurse theorists whose works could be considered aligned with the human sciences explicitly address the notion of intentionality within their writings (see for instance, Newman, 1994; Rogers, 1970). However, the notion of intentionality is implied in, or at least, consistent with, their works and those of their disciples. As noted elsewhere (Pilkington, 2000), the word intentionality has been in use since medieval times and there are various definitions of the concept in the literature, reflecting different philosophical paradigms or worldviews. In this column, I explore the notion as it appears in two nursing perspectives, Parse’s (1981, 1998) theory of human becoming and Watson’s (1985, 1999, 2005) theory of human caring. Both of these theorists have spoken about how their theoretical thinking grew out of their own life experiences as well as through engaging with the world of ideas (Fuld Institute for Technology in Nursing Education, 1997a, 1997b). And so, I begin by identifying key theoretical influences that shaped their thinking about intentionality. In addition, I will explore
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.004 | 0.045 |
| Scholarly communication | 0.009 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
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".