The Internal-External Dynamic: Using Research to Inform Government Policy about Poverty in Canada
Bibliographic record
Abstract
Background: The purpose of this study is to describe integrated knowledge translation (iKT) partners’ experiences with moving knowledge to action within government. In this study, iKT partners emerged from government, academic, and community settings with a shared interest in making changes to policies, programs, and services that would benefit families in poverty. Methods: Interview data were generated with 23 iKT partners who worked within or close to municipal and provincial governments. Partners were asked about how to use research findings to draw attention to and make needed changes within government. Qualitative description was used to answer our research question. An iterative and inductive process of coding, categorizing, and theming characterized our analysis. Results: Partners described how bureaucracy stymied change as well as how bureaucratic barriers could be overcome. In particular, partners described how to create opportunities for research use through an internal-external dynamic/dance, wherein research is strategically poised to address current political priorities. The value-laden nature of poverty also has implications for research use. Conclusion: An interplay of public engagement and socially accountable partnerships are needed to drive change within government. The broad shift in academia to engage with community and government partners warrants further discussion.
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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 | 0.067 | 0.076 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.045 | 0.037 |
| Scholarly communication | 0.027 | 0.008 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".