evaluate the impact of multi-sector partnerships
Bibliographic record
Abstract
Kathleen A Martin Ginis3, Paul A Estabrooks4 and Amy E Latimer-Cheung2 Background: The RE-AIM (Reach, Effectiveness, Adoption, Implementation, and Maintenance) framework is a reliable tool for the translation of research to practice. This framework has been widely applied to assess the impact of individual interventions. However, RE-AIM has rarely been used to evaluate implementation interventions, especially from multi-sector partnerships. The primary purpose of this paper is to operationalize the RE-AIM approach to evaluate large, multi-sector partnerships. SCI Action Canada, a community-university partnership aimed to promote physical activity among adults with spinal cord injury, is used as an example. A secondary purpose is to provide initial data from SCI Action Canada by using this conceptualization of RE-AIM. Methods: Each RE-AIM element is operationalized for multi-sector partnerships. Specific to SCI Action Canada, seven reach calculations, four adoption rates, four effectiveness outcomes, one implementation, one organizational maintenance, and two individual maintenance outcomes are defined. The specific numerators based on SCI Action Canada activities are also listed for each of these calculations. Results: The results are derived from SCI Action Canada activities. SCI Action Canada’s reach ranged from 3% (end-user direct national reach) to 37 % (total regional reach). Adoption rates were 15 % (provincial level adoption) to 76 % (regional level adoption). Implementation and organizational maintenance rates were 92 % and 100%, respectively. Conclusions: We have operationalized the RE-AIM framework for larger multi-sectoral partnerships and demonstrated its applicability to such partnerships with SCI Action Canada. Future partnerships could use RE-AIM to assess their public health impact.
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
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | high |
| gpt | Insufficient payload (model declined to judge) Domain: not available · Genre: Other About the Canadian research system: no · About a Canadian topic: no | Not applicable | low |
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 | 0.141 | 0.243 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.003 | 0.018 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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, unvalidatedLabeled directly by 2 models reading the full record.
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".