Self-Management Education in Diabetes; How do We Measure the Effectiveness of Diabetes Expo in Improving Self-Management Skills of Individuals Affected by Diabetes?
Bibliographic record
Abstract
The Canadian diabetes association is working to improve the quality of life of people living with diabetes through self-management education, research, and advocacy. Continued self-management education is the cornerstone in helping individuals living with diabetes. One of the important parameters in determining the effectiveness of any self-management education intervention is its ability to modify a risky health behavior. The Diabetes expo is one of the several projects implemented by Canadian Diabetes Association (CDA) that are aimed towards improvement of the self-management skills of people living with diabetes. The purpose of this study is to examine the effectiveness of a community diabetes self-management event and annual expo event hosted by CDA is discussed as a case study. First, review of previous documents and reports available with organization was done. Further, key informant interviews were conducted to identify the intended outcomes for evaluation of effectiveness of diabetes expo. To complete the task of the evaluation, logic model was used. In the next step indicators were identified which will measure the outcomes of the programme. In order to do that review of the Canadian Diabetes educator sections reference guide on best practices and critical literature review was also conducted. Review of the survey questionnaire used by CDA revealed the need for evaluation methods which will generate data for measuring the intended outcomes of diabetes expo. Constructs of health belief model (HBM) were employed in building the evaluation tool for the intended participants in a three parts survey questionnaire. The objective was to generate important data which will help project coordinators measure the change in perceptions along with other intended short term outcomes of the project. Finally, the data from the previous evaluation report was compared with the short term outcomes identified from the key informant interviews, critical literature review, and review of other documents. The data shows that diabetes expo is very popular amongst the intended participants. However its effectiveness is mainly determined by the achievement of the expected intended short term outcomes (changes in perception areas identified by HBM, empowerment) of a self-management education event.\n
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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 | 0.017 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".