The applicability and transferability of public health research from one setting to another: a survey of maternal health researchers
Bibliographic record
Abstract
BACKGROUND: Little is known about the process of assessing whether research conducted in one setting is applicable (i.e. implementable) and transferable (i.e. as effective) to another, despite its importance for health policy and practice. Applicability/transferability differs from external validity; the former focuses on potential utility in another specific setting, whilst the latter is more general. This study explored perceptions of applicability/transferability among maternal health researchers. METHODS: Published maternal public health researchers in low- or middle-income countries were invited to complete an online questionnaire. They were shown four summaries of maternal public health intervention evaluations and asked which they felt were the most and least applicable/transferable to their own setting and why. RESULTS: 283 valid questionnaires were received (41% response rate). Applicability/transferability decisions frequently depended on the pertinence of the problem addressed by the intervention or the intervention's characteristics. Less common were comparison of the respondents' setting with the study setting, or consideration of the study's effectiveness. CONCLUSIONS: The factors affecting perceptions of applicability/transferability are broader than those associated with external validity. Improving the reporting of intervention characteristics and implementation is particularly important for applicability/transferability assessments and could increase the appropriate use of public health research in policy and practice.
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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 | 0.247 | 0.422 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".