Evaluating the Codesigning Dementia Diagnosis and Post‐Diagnostic Care (COGNISANCE) project
Bibliographic record
Abstract
Abstract Background Persons with dementia and their family/friend care partners often report poor experiences at the time a dementia diagnosis was given and in the supports provided following the diagnosis. Such experiences often include a lack of information, a lack of support, and limited or no hope. While there are national guidelines that outline the steps required to make a dementia diagnosis, there is limited information about how best to engage persons with dementia and their care partners and communicate this information to them. There are also few resources to support individuals and care partners in navigating this challenging journey. The "Codesigning dementia diagnosis and post‐diagnostic care" (COGNISANCE) project aims to develop and evaluate toolkits and behaviour change campaigns for: 1) members of the at‐risk public and 2) health and social care providers to improve the dementia diagnosis experience and the supports provided following a diagnosis. The purpose of this poster is to describe the process of developing an evaluation plan, together with persons living with dementia and their care partners, that will be implemented in the five countries participating in COGNISANCE: Canada, Australia, Netherlands, United Kingdom, and Poland. Method The evaluation plan is being informed by social marketing theories and guided by the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE‐AIM) framework. Working collaboratively with persons living with dementia and care partners, and a large international team, we are developing a comprehensive plan to evaluate the implementation and effectiveness of the behaviour change toolkits and campaigns. Result We will describe the process used to develop the evaluation plan. This includes our experiences working together with persons living with dementia and their care partners, as well as our international collaborators, to develop a plan that achieves our objectives and is feasible to implement across the participating countries. Conclusion Findings from the COGNISANCE evaluation will inform opportunities to spread the toolkits and campaigns within the participating countries, as well as the development of an implementation playbook, a resource aimed to support developing countries in creating and implementing their own toolkits and campaigns.
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 | 0.090 | 0.089 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".