How do we know when research from one setting can be useful in another? A review of external validity, applicability and transferability frameworks
Bibliographic record
Abstract
OBJECTIVE: To review published frameworks that included criteria for the assessment of external validity, applicability and transferability in their assessment of health research. METHODS: Five databases were searched for articles relating to the assessment of external validity or applicability and transferability in health research. A coding framework was developed inductively and used to assess which types of criteria were included in the frameworks. RESULTS: Thirty-eight articles describing 25 frameworks were identified. Eleven focused solely on the assessment of applicability and transferability; 14 presented more general decision-making or evidence appraisal frameworks. The criteria were synthesized into four main categories: setting, intervention, outcomes and evidence. None of the frameworks covered all the criteria identified. A major limitation was the lack of empirical data used to develop many frameworks and the apparent lack of assessment of their perceived utility. CONCLUSION: A validated framework of applicability and transferability would help those aiming to encourage research use, as well as those conducting research. Greater understanding of applicability and transferability could help to encourage the appropriate use of research and the development of research that is more useful.
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 | 0.643 | 0.854 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.015 | 0.013 |
| Bibliometrics | 0.050 | 0.039 |
| Science and technology studies | 0.004 | 0.022 |
| Scholarly communication | 0.022 | 0.042 |
| Open science | 0.008 | 0.017 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".