Collaborating to co-produce educational content to champion dementia care in acute settings:Lessons learned
Bibliographic record
Abstract
Enhancing dementia care to benefit people with dementia and their family carers requires both global and local initiatives. The purpose of this presentation is to share how we adapted a successful Scottish initiative (known as the Scottish National Dementia Champions Programme) to our local Canadian context with the ultimate aim to ‘champion’ dementia care provided by healthcare professionals in acute care settings. To guide our co-production, we are employing Hawkins et al.’s three-phase framework: (1) evidence review and stakeholder consultation, (2) co-production of intervention content, and (3) prototyping. After phase one was completed (in February 2020) we embarked on phase two and learned several key lessons from the co-production of the program content. These include the importance of (a) partnering with those with lived experience to infuse their voices in all aspects of the program; (b) learning and benefitting from our Scottish colleagues’ rich experiences; (c) capitalizing on long-standing Pan-Canadian relationships and launching new ones; (d) meeting virtually, on a consistent basis over 12 months, with established agendas and precise minute-taking; and (e) building consensus on what and how to prioritize program content and resources to align with the Canadian Charter of Rights for People with Dementia, within the foundation of person-/family-centred care. The results of our work in phase two enables us to proceed with phase three to pilot the co-produced program’s content and resources in the Canadian province of Saskatchewan.
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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 | 0.100 | 0.107 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.006 | 0.020 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".