PDA and smartphone use by individuals with moderate-to-severe memory impairment: Application of a theory-driven training programme
Bibliographic record
Abstract
We describe a structured, theory-driven training programme for individuals with moderate-to-severe memory impairment in the use of emerging commercial technology. We demonstrate its application to 10 individuals with memory impairment from a variety of aetiologies. A within-subject, ABAB multi-case experimental design was used to evaluate the impact of personal digital assistant or smartphone use on day-to-day memory functioning at baseline, immediately post-intervention, at return to baseline, and at short-term follow-up (range = 3-8 months). An errorless fading-of-cues protocol enabled all participants to acquire the skill set necessary to operate their PDA or smartphone independently. All 10 individuals showed robust improvement in day-to-day functioning post-intervention as quantified across a number of ecologically valid questionnaire and task-based measures. This was further corroborated by family members with whom six of the participants resided. These findings demonstrate that individuals with moderate-to-severe memory impairment can acquire the skills necessary to independently, flexibly and broadly apply commercial technology to support their everyday memory functioning. Moreover the findings confirm that the gap between individuals with memory impairment and potent emerging technology can be closed by the application of a systematic theory-driven training programme.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".