Strategies for balancing internal and external validity in evaluations of interventions
Bibliographic record
Abstract
BACKGROUND: Evaluations of interventions should be carefully designed and conducted to maintain a balance between internal and external validity, with the dual goal of minimising the influence of potential confounders and improving the generalisability or applicability of any findings to practice. AIM: To review strategies to promote balance between internal and external validity in an evaluation of a cognitive-behavioural intervention for chronic insomnia. DISCUSSION: A pragmatic approach is needed to balance internal and external validity, and generate evidence relevant to practice. The authors present strategies to promote such a balance, including using strict eligibility criteria, subgroup analysis, random assignment of patients based on preferences, a no-treatment control condition, and standardised and consistent implementation of the intervention. CONCLUSION: A balance between internal and external validity is essential to promote enrolment in the study and confidence in attributing its outcomes to an intervention, as well as to provide answers to clinically relevant questions such as who benefits most from which intervention. IMPLICATIONS FOR PRACTICE: The authors recommend researchers conduct a pilot study in advance of an evaluation, to help decide which strategies to use and how to balance internal and external validity.
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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 | 0.838 | 0.909 |
| Meta-epidemiology (narrow) | 0.007 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.024 |
| Bibliometrics | 0.022 | 0.015 |
| Science and technology studies | 0.005 | 0.022 |
| Scholarly communication | 0.018 | 0.018 |
| Open science | 0.009 | 0.015 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 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".