Supporting Evidence-Informed Practice in Human Service Organizations: An Exploratory Study of Link Officers
Bibliographic record
Abstract
Human service organizations seeking to infuse research and other forms of evidence into their programs often need to expand their knowledge sharing systems in order to build their absorptive capacities for new information. To promote their engagement in evidence-informed practice, human service organizations can benefit from connections with intermediary organizations that assist with the dissemination and utilization of research and the use of internal knowledge brokers, called link officers. These boundary-spanning individuals work to embed external research and internal evidence in order to address current organizational priorities and service demands. This exploratory study describes the characteristics, major activities, and perceptions of link officers connected with three pioneering intermediary organizations. Quantitative and qualitative data from a survey of 137 Canadian and UK link officers provide a profile of these professionals, including how they engage practitioners to promote evidence-informed practice and the degree to which they are supported within their organizations and by intermediary organizations. The article concludes with practice and research implications for the development of the link officer role in human service organizations.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.017 | 0.009 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 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".