Using Single-Case Experiments to Support Evidence-Based Decisions
Bibliographic record
Abstract
For practitioners, the use of single-case experimental designs (SCEDs) in the research literature raises an important question: How many single-case experiments are enough to have sufficient confidence that an intervention will be effective with an individual from a given population? Although standards have been proposed to address this question, current guidelines do not appear to be strongly grounded in theory or empirical research. The purpose of our article is to address this issue by presenting guidelines to facilitate evidence-based decisions by adopting a simple statistical approach to quantify the support for interventions that have been validated using SCEDs. Specifically, we propose the use of success rates as a supplement to support evidence-based decisions. The proposed methodology allows practitioners to aggregate the results from single-case experiments to estimate the probability that a given intervention will produce a successful outcome. We also discuss considerations and limitations associated with this approach.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Other design | high |
| opus | Metaresearch Domain: Methods · Genre: Methods About the Canadian research system: no · About a Canadian topic: no | Theoretical or conceptual | medium |
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 | 0.734 | 0.864 |
| Meta-epidemiology (narrow) | 0.005 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.013 | 0.006 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.011 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".