Observational Outcome Measures to Evaluate Assistive Technology Use by People with Dementia - Report Series # 12
Bibliographic record
Abstract
This report describes a digital video-audio behavioural observation methodology for use in a naturalistic setting to evaluate communication rehabilitation interventions for older adults with dementia. Behavioural observation via recorded video-audio offers a number of advantages over other data collection methodologies, which can be subject to a number of biases and limitations, some of which are discussed. In this study, high quality digital audio-video recordings were collected on participants attending a respite care day program. Recording equipment was inconspicuously placed, and measurement occurred either during normal day-to-day activities or during more directed activities (e.g., playing bingo). The recordings can be used to document the occurrence of behaviours and paired behaviours of interest over extended periods of time or selected samples of interest can be downloaded for detailed analysis including lag-sequential analyses. It was found that behavioural observation can complement traditional objective measures of impairment and subjective questionnaire measures, in accordance with the World Health Organization’s (WHO) International Classification of Functioning, Disability, and Health (2001).
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".