What hinders and helps academics to conduct Dissemination and Implementation (D&I) research in the field of nutrition and physical activity? An international perspective
Bibliographic record
Abstract
BACKGROUND: Ineffective research-practice translation is a major challenge to population health improvement. This paper presents an international perspective on the barriers and facilitators associated with the uptake of and engagement in Dissemination and Implementation (D&I) research in the fields of physical activity and nutrition. METHODS: A mixed methods study involving participants from the International Society for Behavioral Nutrition and Physical Activity (ISBNPA) network. Participants completed an online survey (May-July 2018) and/or participated in a focus group during the annual ISBNPA conference (June 2018). Descriptive statistics were generated for quantitative online and pre-focus group survey data. Fisher's exact tests investigated associations of (i) length of time in academia, (ii) career stage and (iii) country of work, and agreement with 'perceptions of D&I'. Qualitative data were analysed thematically. RESULTS: In total, 141 participants responded to the survey (76% female, 21% aged 35-39 years, 14 countries represented) and 25 participated in focus groups (n = 3). Participants self-identified as having knowledge (48%), skills (53%) and experience supporting others (40%) to conduct D&I research. The majority (96%) perceived D&I was important, with 66% having organizational support for D&I, yet only 52% reported prioritizing D&I research. Perceptions of D&I differed by length of time in academia, career stage and country of work. Barriers included: (i) lack of D&I expertise; (ii) lack of organisational support/value for D&I; (iii) embedded scientific beliefs/culture; (iv) methodological challenges with D&I research; (v) funding/publishing priorities and; (vi) academic performance structures. Facilitators included: (i) increased presence/value of D&I; (ii) collective advocacy; (iii) organisational support for D&I; (iv) recruitment of D&I scientists and; (v) restructure of academic performance models, funding/publishing criteria. CONCLUSIONS: Individual, organisational and system-wide factors hindered academics' engagement with and support for D&I research, which was perceived to reduce opportunities for research-practice translation. Factors were mostly consistent across countries and individual career stages/time spent in academia. Embedding D&I early within academic training, and system-wide reorientation of academic performance and funding structures to promote and facilitate D&I research, are some of the necessary actions to reduce the research-practice gap. Consistent with public health more broadly, these changes are long overdue in the fields of physical activity and nutrition.
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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 | 0.211 | 0.255 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.020 |
| Scholarly communication | 0.033 | 0.018 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".