The development of the Evaluation Toolkit for the Alberta Healthy Communities Approach (AHCA)
Bibliographic record
Abstract
Background: An evaluation toolkit was developed by Alberta Health Services to support rural community members in determining and evaluating the outcomes of healthy community initiatives from the Alberta Healthy Communities Approach (AHCA) project. Approach: Many community leaders and volunteers in Alberta rural communities are heavily involved in the health promotion and community development work. While they have a strong understanding of the needs and strengths of their communities and are highly capable of implementing healthy community initiatives, community members may lack the capacity and/or resources for evaluation. Understanding the process, outcome, and impact of healthy community initiatives and sharing success stories to wider audience are vital to implementation and sustainability. To address this gap, the Cancer Prevention and Screening Innovation (CPSI) unit at Alberta Health Services developed an evaluation toolkit to support community partners in evaluating their own initiatives. The evaluation toolkit includes key features such as the purpose and importance of conducting evaluation, a decision tree to help determine the most appropriate evaluation methods for different types of initiatives, assessment templates that are customizable based on the communities needs and preferences, and a survey bank that provides questions to be incorporated into the assessment templates. It aims to build community capacity by providing resources such as customizable data collection tools like surveys, observational assessments, and interview questions, and other creative and culturally sensitive data collection methods. The toolkit also aims to reduce barriers and facilitate collection of meaningful feedback from community members, and to better document the impact of the initiatives. Results: With the guidance of the evaluation toolkit, it strengthened capacity of leaders from 9 rural communities to develop and take ownership of evaluating their healthy community initiatives. As a result, over twenty community surveys involving 500+ respondents were conducted. Community members became more engaged and informed in the process of evaluation, with some results being presented to leadership and town hall meetings. Several communities also utilized these results to apply for additional grants to continue their health promotion efforts. Additionally, twelve digital stories showcasing the AHCA project were posted online, further disseminating the project's impact. Implications: The development and utilization of the evaluation toolkit played a pivotal role in ensuring that communities to effectively implement and evaluate their health promotion initiatives. By building community capacity and providing a user-friendly solution for evaluation, the CPSI aims to bridge the gap between implementation and evaluation. The AHCA project continues to support partnering communities in sharing their visions and hard work by measuring progress, disseminating results through various channels, and engaging diverse stakeholders.
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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 | 0.205 | 0.142 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.007 | 0.013 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".