Exploring the use of feminist philosophy within nursing research to enhance post‐positivist methodologies in the study of cardiovascular health
Bibliographic record
Abstract
Nursing has historically relied heavily on scientific knowledge. It is not surprising that the cardiovascular health literature has been highly influenced by the post-positivist philosophy. The nursing discipline, as well as the cardiovascular nursing specialty, continues to benefit from research grounded within this philosophical tradition. At the same time, there are limitations associated with post-positivism. Therefore, it is beneficial for researchers and clinicians to examine the potential contributions various philosophical traditions can have for their research and practice. This paper is an exploration of the compatibilities of feminist and post-positivist philosophies in the study of cardiovascular nursing research. The ensuing discussion entails an examination of my clinical and research interests, the grounding of my research within the post-positivist perspective and the significant contribution feminist philosophy can make to my research.
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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 | 0.061 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.041 |
| Scholarly communication | 0.010 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".