Challenges and Considerations in Naming True and Quasi-Experimental Research Designs: A Methodological Discussion
Bibliographic record
Abstract
Background: Novice researchers may face challenges in choosing names for true and quasi-experimental designs due to complexity in terminology and variety of experimental designs used in nursing. Addressing these issues is crucial for ensuring clarity and accuracy in experimental nursing research. Aim: To discuss the complexities, challenges, and considerations involved in naming true and quasi-experimental research designs and propose a decision tree for researchers to guide them in accurately and consistently naming these designs. Design: A methodological discussion. Methods: Research texts, the Public Health Agency of Canada Critical Appraisal Tool Kit, and articles from various scientific journals were chosen to illustrate the challenges and characteristics of different experimental and quasi-experimental study designs. Discussion: Key characteristics of true and quasi-experimental designs such as nature of experimental and control groups and process of random allocation are outlined and illustrated with examples. Conclusion: A decision tree is offered to help researchers and reviewers in the precise and consistent labeling of true and quasi-experimental designs. By providing a structured way for decision-making, it enhances the accuracy and reliability of classification processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.820 | 0.853 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.016 | 0.020 |
| Science and technology studies | 0.010 | 0.039 |
| Scholarly communication | 0.022 | 0.032 |
| Open science | 0.014 | 0.016 |
| Research integrity | 0.016 | 0.017 |
| 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".