[Mixed methods: promising strategies for the evaluation of nursing interventions].
Bibliographic record
Abstract
Based on a survey of the literature in human and nursing sciences and illustrated with concrete research examples, we will identify promising research directions for mixed-methods studies and present strategies for applying this type of research design to the evaluation of nursing interventions. This article provides three examples of mixed-methods design that utilize schematic representation about evaluation of nursing interventions. Based on examples, the issues discussed are: (1) sufficient significance for research program to invest the required human and material resources, (2) reason for using qualitative and quantitative data simultaneously or sequentially, (3) integration of qualitative and quantitative data when the participants are from different target populations; (4) presentation of the findings.
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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 | 0.385 | 0.422 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.013 | 0.014 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".