Postpositivist critical multiplism: Its value for nursing research
Bibliographic record
Abstract
AIM: Following persistent criticisms of logical positivism, postpositivism emerged as a philosophy of science for developing nursing knowledge. Here, we offer a discussion of postpositivist critical multiplism and its value to nursing research. DESIGN: Discussion paper. METHODS: We searched relevant literature published between 1978-2018, indexed in CINAHL, MEDLINE, PubMed or PsychINFO. Findings are discussed in the context of stroke nursing research. RESULTS: Postpositivist critical multiplism acknowledges the importance of human influence on knowledge development and enables nurse researchers to use multiple approaches to address complex human phenomena. In doing so, the unique perspectives of stakeholder groups including patients, family members, knowledge users can be respected. There are certain steps that can be used by critical multiplist investigators to approach stroke nursing research. Using these steps and the critical multiplist approach may minimize preconceptions or biases associated with using one research method over another and maximize confidence in resultant research knowledge.
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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 | 0.265 | 0.314 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.007 | 0.080 |
| Scholarly communication | 0.025 | 0.033 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".