Can nursing epistemology embrace <i>p</i>‐values?
Bibliographic record
Abstract
The use of correlational probability values (p-values) as a means of evaluating evidence in nursing and health care has largely been accepted uncritically. There are reasons to be concerned about an uncritical adherence to the use of significance testing, which has been located in the natural science paradigm. p-values have served in hypothesis and statistical testing, such as in randomized controlled trials and meta-analyses to support what has been portrayed as the highest levels of evidence in the framework of evidence-based practice. Nursing has been minimally involved in the rich debate about the controversies of treating significance testing as evidentiary in the health and social sciences. In this paper, we join the dialogue by examining how and why this statistical mechanism has become entrenched as the gold standard for determining what constitutes legitimate scientific knowledge in the postpositivistic paradigm. We argue that nursing needs to critically reflect on the limitations associated with this tool of the evidence-based movement, given the complexities and contextual factors that are inherent to nursing epistemology. Such reflection will inform our thinking about what constitutes substantive knowledge for the nursing discipline.
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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 | 0.207 | 0.340 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.009 | 0.132 |
| Scholarly communication | 0.036 | 0.053 |
| Open science | 0.009 | 0.019 |
| Research integrity | 0.023 | 0.034 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".