Bibliographic record
Abstract
Problem, research strategy and findings The number of people living with dementia (PLWD) is set to increase to 132 million by 2050, with most expected to reside in their own homes, not congregate living settings. Limited research on the impact of the built environment on PLWD has focused on planning outcomes, with no research on access to the planning process that shapes the places they live in. In this study I ask: what are the barriers and facilitators to participation for PLWD at open houses? I accompanied seven PLWD (individually or in pairs) to open houses, a commonly used public engagement tool, in Waterloo (Canada). To capture the experiences, I used audio recordings, field notes and sketches, photographs, and a postexperience interview with participants. Accessibility of public engagement tools for PLWD can be improved by ensuring respectful and patient communication (not rushing attendees, using plain language); providing clear, concise presentation materials (less is more, offering in-the-moment feedback opportunities); and using a familiar, comfortable physical location (sensitive to sensory overstimulation through acoustics and lighting). The sense of inclusion participants felt in attending the open houses was unexpected.Takeaway for practice The open house is already well suited to the accessibility needs of PLWD, with peripheral, circular layouts allowing participants to learn at their own pace and interact one on one with practitioners. However, the recommendations that would make public engagement tools more accessible to PLWD are easily implementable, and by educating planners in these techniques there could be an opportunity for the profession to help dismantle the stigma associated with dementia. The commonly used public engagement tools used during the planning process need to be universally accessible so PLWD and other people with disabilities can attend any meeting they choose and have an impact on decision making in their communities.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.016 | 0.028 |
| Scholarly communication | 0.015 | 0.017 |
| Open science | 0.003 | 0.016 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.021 | 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".