Collaboration for Spread Handbook: An Approach to Guiding Spread of Successful Community-based Interventions
Bibliographic record
Abstract
This Collaboration for Spread Handbook grew out of our experiences in the IMPACT research program. The Innovative Models Promoting Access-to-Care Transformation (IMPACT) initiative was a five-year Canadian-Australian research program that provided an opportunity to build new and existing partnerships, programs, and research to co-create models of care that enhance access and ultimately improve health outcomes for vulnerable populations.This Handbook is intended for people who are engaging in collaborative community-based research to advance primary healthcare (i.e., partners including health systems, community agencies, and researchers). The Handbook builds on existing knowledge and is intended to provide an initial introduction to community-based collaborative research. It will be of interest to all stakeholders involved in community-based collaborative research with particular emphasis on supporting researchers to collaborate. An intent of this Handbook is to promote better understanding of the knowledge, skill, and resource requirements needed to develop and sustain community-based research collaboration and partnerships.This Handbook has an accompanying Pop-Up Implementation Guide https://doi.org/10.26181/28622168
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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 | 0.117 | 0.116 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.013 | 0.009 |
| Scholarly communication | 0.020 | 0.017 |
| Open science | 0.010 | 0.025 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.044 | 0.020 |
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".