When can research from one setting be useful in another? Understanding perceptions of the applicability and transferability of research
Bibliographic record
Abstract
Determining whether research findings from one setting are relevant to another is complex and poorly understood. This study aimed to explore the factors affecting whether research from other settings was perceived to be of potential use to those working in or researching maternal health in Ghana. Semi-structured interviews were conducted with 69 purposively sampled government decision-makers, researchers and other stakeholders working in maternal health in Ghana in 2008-09. The most influential factors affecting perceptions of applicability/transferability were the study's congruence with interviewees' previous experiences and beliefs. Interventions' adaptability was also considered crucial (and more important than remaining faithful to the original intervention). However, it was frequently considered a distinct stage in the research use process rather than a consideration of applicability/transferability. More attention was paid to the implementability of the intervention in the new setting, than to whether it would be as effective there. Interpretations of intervention descriptions and evaluation findings varied between interviewees, even when the same information was presented. This study is one of the first to explore perceptions of applicability/transferability of public health research among researchers and potential research users in a low-income setting. The findings suggest that existing frameworks of applicability/transferability do not reflect the factors considered to be most important in Ghana.
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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 | 0.473 | 0.620 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.008 | 0.053 |
| Scholarly communication | 0.027 | 0.040 |
| Open science | 0.004 | 0.020 |
| Research integrity | 0.008 | 0.010 |
| Insufficient payload (model declined to judge) | 0.002 | 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".