The Significance of Gender in Phenomenological Nursing Research
Bibliographic record
Abstract
The aim of this paper is to discuss in the light of phenomenological philosophy, whether it can be argued that men and women have different lifeworlds and how this may legitimize the segregation of men and women in empirical nursing research. We analyzed peer-reviewed papers from 2003-2012 and scrutinized the arguments used for dividing men and women into separate groups in empirical nursing studies based on phenomenology. We identified 24 studies using gender segregation and posed the following questions: 1. What is the investigated phenomenon as explicated by the authors? 2. What arguments do the authors use when dividing participants into gender specific groups? The analysis showed that a variety of phenomena were investigated that were all related to a specific medical condition. None appeared to be gender-specific, though the authors argued for a sole focus on either women or men. The most common argument for segregating men and women were reference to earlier studies. A few studies had references to methodology and/or philosophy as argument for a segregation of men and women. Arguments for gender segregation in empirical nursing studies based on a phenomenological approach tend to build on the conviction that experiences of health related phenomena are gendered. However, it seems to be difficult to identify conclusive arguments for this division within phenomenological philosophy. Therefore we recommend that segregation should be used with caution. Otherwise other research approaches may be more suitable.
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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 | 0.115 | 0.135 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.015 | 0.077 |
| Scholarly communication | 0.020 | 0.026 |
| Open science | 0.002 | 0.016 |
| Research integrity | 0.004 | 0.004 |
| 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".