Factors that Facilitate and Impede Effective Knowledge Translation in Population Health Promotion: Results from a Consultation Workshop in Iran
Bibliographic record
Abstract
BACKGROUND: The workshop that this paper reports, held in Iran in May of 2011, at the 1st Inter-national and 4th National Congress on Health Education and Promotion, had three main objec-tives: 1) to introduce participants to the knowledge translation (KT) concept, along with its mod-els and methods; 2) to enhance participants' knowledge of how KT could apply to public health education and promotion ; and 3) to learn from different participating stakeholder groups about the factors that facilitate or impede effective KT in public health education and promotion in Iran. METHODS: The workshop consisted of three components: introducing the KT concept, assessing the KT capacity of participants, and facilitating a discussion of the important contextual factors that promote and impede effective KT. Of the 26 individuals from across the country participat-ing in the workshop, 17 took part in a KT capacity assessment activity. They classified them-selves into one of the following three stakeholder groups: administrators and policymakers (n=6), practitioners (n=2), and researchers (n=9). RESULTS: There were different capacities for KT across the three stakeholder groups. The re-ported challenges for effective KT include "lack of resources and funding"; "lack of time"; "poor quality of relationships and lack of trust between health policymakers, administrators, re-searchers, and clinicians"; "inadequate skills possessed by healthcare professionals and adminis-trators for assessment and adaptation of research findings"; and "poor involvement of commu-nity partners in the research process." DISCUSSION: There is a great need to develop effective strategies to overcome the reported barri-ers for effective KT.
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 | 0.047 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".