Bibliographic record
Abstract
AIM: To assist the researcher in understanding the similarities and differences between the Husserlian and Heideggerian philosophies of phenomenology, and how that philosophy can inform nursing research as a useful methodology. BACKGROUND: Nurse researchers using phenomenology as a methodology need to understand the philosophy of phenomenology to produce a research design that is philosophically congruent. However, phenomenology has a long and complex history of development, and may be difficult to understand and apply. DATA SOURCES: The author draws from Heidegger (1962), Gadamer (2004), and nurse scholars and methodologists. DISCUSSION: To give the reader a sense of the development of the philosophy of phenomenology, the author briefly recounts its historical origins and interpretations, specifically related to Husserl, Heidegger and Gadamer. The author outlines the ontological and epistemological assumptions of Husserlian and Heideggerian phenomenology and guidance for methodology inspired by these philosophers. Difficulties with engaging in phenomenological research are addressed, especially the processes of phenomenological reduction and bracketing, and the lack of clarity about the methods of interpretation. CONCLUSION: Despite its complexity, phenomenology can provide the nurse researcher with indepth insight into nursing practice. IMPLICATIONS FOR PRACTICE/RESEARCH: An understanding of phenomenology can guide nurse researchers to produce results that have meaning in nursing patient care.
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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 | 0.137 | 0.123 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.014 | 0.097 |
| Scholarly communication | 0.035 | 0.047 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.012 | 0.017 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".