Nursing perspectives on Integral Theory in nursing practice and education: An interpretive descriptive study
Bibliographic record
Abstract
While for decades nursing has advocated for theory-informed practice, more recent attention has tended to focus on mid-range theory rather than the earlier focus on developing grand theory to encompass all of nursing practice. However, there has been continued interest in the holistic nursing community on grand theory and, in particular, on Integral Theory. Although Integral Theory's four-quadrant (AQAL) perspective is familiar in nursing, little is known about how it is being used by nurses in direct practice. The purpose of this interpretive descriptive study was to provide a practice-based perspective on Ken Wilber's Integral Theory in professional nursing practice. The following research question was investigated: How does Integral Theory assist nurses in describing and understanding their professional work? Nurses participating in this study used Integral Theory as a map or heuristic that gave structure to an inquiry process in professional nursing practice and in nursing education in a manner that was holonic, multiperspectival, and self-reflective. Challenges constraining nurses' use of Integral Theory included its intricacy, as well as contextual factors in practice environments. Implications for nursing practice and education for the use of Integral Theory's meta-framework are described.
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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 | 0.021 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".