A pragmatic evaluation of a public health knowledge broker mentoring education program: a convergent mixed methods study
Bibliographic record
Abstract
Abstract Background Public health professionals are expected to use the best available research and contextual evidence to inform decision-making. The National Collaborating Centre for Methods and Tools developed, implemented, and evaluated a Knowledge Broker mentoring program aimed at facilitating organization-wide evidence-informed decision-making in ten public health units in Ontario, Canada. The purpose of this study was to pragmatically assess the impact of the program. Methods A convergent mixed methods design was used to interpret quantitative results in the context of the qualitative findings. A goal-setting exercise was conducted with senior leadership in each organization prior to implementing the program. Achievement of goals was quantified through deductive coding of post-program interviews with participants and management. Interviews analyzed inductively to qualitatively explain progress toward identified goals and identify key factors related to implementation of EIDM within the organization. Results Organizations met their goals for evidence use to varying degrees. The key themes identified that support an organizational shift to EIDM include definitive plans for participants to share knowledge during and after program completion, embedding evidence into decision-making processes, and supportive leadership with organizational investment of time and resources. The location, setting, or size of health units was not associated with attainment of EIDM goals; small, rural health units were not at a disadvantage compared to larger, urban health units. Conclusions The Knowledge Broker mentoring program allowed participants to share their learning and support change at their health units. When paired with organizational supports such as supportive leadership and resource investment, this program holds promise as an innovative knowledge translation strategy for organization wide EIDM among public health organizations.
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 | 0.133 | 0.116 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| 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".