Bibliographic record
Abstract
AIM: This paper targets novice nurse researchers to highlight how the perspectives of human sciences are useful in understanding people's experiences. BACKGROUND: There is a need to address the utility of human sciences or the humanistic philosophy that values the understanding of subjective experiences in nursing, given that the mainstream development of nursing knowledge is still influenced by the positivist and post-positivist research paradigms. DATA SOURCES: Discussion papers on Heideggerian hermeneutic phenomenology, human sciences, and qualitative research were accessed through the databases Cinahl and Medline over the past 30 years. Seminal works on phenomenology were addressed in this paper. DISCUSSION: Using Heideggerian hermeneutic phenomenology as a commonly referenced human philosophy and methodology, this paper discusses how Heidegger's (1962) perspective may be used in nursing practice and research. Van Manen's (1990) descriptions of phenomenological science are discussed to address the perspective's value in nursing inquiry and to reveal the biases associated with this humanistic approach. CONCLUSION: The limitations of human sciences should not deter nurse researchers from using this type of nursing inquiry as it can provide an important framework in nursing research, practice and knowledge development. IMPLICATIONS FOR RESEARCH/PRACTICE: The author's perspective as a graduate student highlights the importance of human sciences in exploring the experiences of people vital in the delivery of nursing practice. However, researchers wishing to undertake humanistic inquiry should learn the philosophical and methodological underpinnings of their chosen humanistic approach.
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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 | 0.085 | 0.092 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.007 | 0.062 |
| Scholarly communication | 0.022 | 0.018 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.006 |
| 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".