A theory-driven training programme in the use of emerging commercial technology: Application to an adolescent with severe memory impairment
Bibliographic record
Abstract
We describe a theory-driven memory intervention programme for training individuals with moderate to severe memory impairment in the use of emerging commercial technology. Here we demonstrate the application of the programme to training MK, an 18-year-old woman with severe memory impairment following treatment for a suprasellar germinoma, to autonomously use a smartphone to support her day-to-day memory. A within-subject A(1)B(1)A(2)B(2) single-case experimental design was used to evaluate the impact of smartphone use on MK's real-life functioning. Following intervention MK showed increased confidence in dealing with memory-demanding situations and generalised smartphone use across all aspects of her life as quantified by several and varied ecologically valid measures including a phone call schedule, behaviour memory observations and questionnaires. Moreover the intervention also benefited her family as indicated by a sustained reduction in caregiver strain and an increase in reported quality of life. These findings suggest that individuals with severe memory impairment, particularly young adults with potentially life-long dependence on their families, are able to capitalise on emerging commercial technology to function more autonomously. The findings also suggest that the gap between individuals with severe memory impairment and potent emerging technology can be closed by provision of a theory-driven structured training programme.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 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".