Technology in the Lives of Women Who Live With Memory Impairment as a Result of a Traumatic Brain Injury
Bibliographic record
Abstract
A large number of individuals who have experienced a traumatic brain injury are women; unfortunately, there is a lack of literature focusing on their treatment preferences. Electronic memory aids have the potential to offer tremendous assistance to increase the independence of individuals with memory impairment; however, the use of electronic memory aids with this female population has not been explored. The objective of this study was to investigate the perceptions and use of electronic memory aids in women with memory impairment as a result of a traumatic brain injury to further their use of this technology to enable their independence. Two focus groups were conducted, each with five women who self-reported a moderate to severe head injury. The primary theme that emerged was the willingness and interest of this sample to use this technology when provided with an appropriate introduction and learning environment. The results reaffirm current literature supporting the use of electronic memory aids with a population with a head injury. Individuals not currently using this technology were motivated to employ electronic memory aids in their daily lives. Further research must be conducted to develop strategies to enable this population's use of electronic memory aids.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".