Bibliographic record
Abstract
ABSTRACTObjectiveThe goal of this study was to engage members of a long-standing Integrated KT collaborative in a process to revitalize team goals and processes. “The Need to Know” Team started in 2001 in Manitoba, to engage knowledge users in the conceptualization, creation, and application of population health research. The team has garnered numerous national awards and citations for its approach. We conducted a survey of team members (N=27), representing all Health Authorities in the province, plus provincial government reps. Questions included frequency of data use and medium (print vs online), how well the team is meeting is goals, open-ended questions about how the team could be more useful to members and their organizations, and their top 3 suggestions to ensure the ongoing success and increase the impact of the team.MethodTwenty-two of 27 members responded to the survey (81.5%) within one week. Responses to questions about how well the team is meeting its existing goals revealed high scores – especially among those goals which lay entirely within the scope of the team’s control (91% extremely or moderately well). Objectives relating to larger-scale impacts on the healthcare system had lower ratings (72% extremely or moderately well), as might have been expected. ResultsOver 75% reported that the team’s work had impacted their organization’s work moderately or a lot. The most commonly cited examples were that the work of the team increased capacity for data analysis/interpretation and research (18%), provided results that were used in staff and/or board meetings (15%), influenced decisions and the discussions leading up to them (15%), influenced the development and use of region-relevant quality indicators (13%), and were used in the ongoing education of health professionals (13%). The open-ended questions regarding optimal next steps solicited a variety of suggestions ranging from developing even richer relationships with existing partners, to including a wider variety of partner organizations (e.g. Indigenous groups); aligning team priorities with those of the provincial government (where feasible); and moving to occasional electronic meetings for appropriate content issues and to increase impact in partner organizations. ConclusionThis exercise in reflection and strategic planning has shown that the team has done an exceptional job in achieving its initial goals, most of which remain relevant, but some of which need revision. More importantly, several creative approaches have been suggested which may increase future impact and enhance both the breadth and depth of the team’s reach.
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 | 0.014 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.004 |
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".