How funding agencies can support research use in healthcare: an online province-wide survey to determine knowledge translation training needs
Bibliographic record
Abstract
BACKGROUND: Health research funding agencies are increasingly promoting evidence use in health practice and policy. Building on work suggesting how agencies can support such knowledge translation (KT), this paper discusses an online survey to assess KT training needs of researchers and research users as part of a Canadian provincial capacity-building effort. METHODS: The survey comprised 24 multiple choice and open-ended questions including demographics, interest in learning KT skills, likelihood of participating in training, and barriers and facilitators to doing KT at work. More than 1,200 people completed the survey. The high number of responses is attributed to an engagement strategy involving partner organizations (health authorities, research institutes, universities) in survey development and distribution. SPSS was used to analyze quantitative results according to respondents' primary role, geographic region, and work setting. Qualitative results were analyzed in NVivo. RESULTS: Over 85 percent of respondents are interested in learning more about the top KT skills identified. Research producers have higher interest in disseminating research results; research users are more interested in the application of research results. About one-half of respondents require beginner-level training in KT skills; one-quarter need advanced training. Time and cost constraints are the biggest barriers to participating in KT training. More than one-half of respondents have no financial support for travel and almost one-half lack support for registration fees. Time is the biggest challenge to integrating KT into work. CONCLUSIONS: Online surveys are useful for determining knowledge translation training needs of researchers, research users and ultimately organizations. In this case, findings suggest the importance of considering all aspects of KT in training opportunities, while taking into account different stakeholder interests. Funders can play a role in developing new training opportunities as part of a broad effort, with partners, to build capacity for the use of health research evidence. Survey results would ideally be complemented with an objective needs assessment based on core competencies, and should be acted on in a way that acknowledges the complexity of knowledge translation in healthcare, existing training activities, and the expertise stakeholders already have but may not refer to as knowledge translation.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: no | Observational | high |
| grok | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | high |
| opus | Metaresearch Domain: Incentives · Genre: Empirical About the Canadian research system: yes · About a Canadian topic: yes | Observational | high |
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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".