Using linking systems to build capacity and enhance dissemination in heart health promotion: a Canadian multiple-case study
Bibliographic record
Abstract
The purpose of this paper is to examine the utility of linking systems between public health resource and user organizations for health promotion dissemination and capacity building, and to identify factors related to the success of linking systems. The design is a parallel-case study using key informant interviews and content analysis of project reports (synthesized qualitative and quantitative data) of three provincial dissemination projects of the Canadian Heart Health Initiative-Dissemination Phase. Each provincial project used linking activities with public health user groups including meetings, skill building, resources, collaboration, networking and research feedback to facilitate capacity building for and implementation of heart health promotion activities. This paper presents empirical examples of linking system designs, activities, and qualitative and quantitative changes in the public health user groups' health promotion capacity, program delivery and sustainability. The findings indicate enhanced health promotion skills, partnerships, resources, infrastructure, and increased programming and sustainability in the targeted public health organizations of all three provincial projects. Identified barriers to the success of linking systems included lack of appropriately skilled personnel, funds, buy-in and leadership. We conclude that linking systems can be flexibly used to build capacity and disseminate health promotion innovations, and suggest conditions for success.
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 | 0.027 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.021 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".