Assessment of sex analysis in studies of technology-based interventions to alleviate caregiver burden among caregivers of persons with dementia
Bibliographic record
Abstract
BACKGROUND: With an increase in the number of family caregivers for persons with dementia, caregiver burden is a major concern. Defined as computer-based devices and programs, technology has been identified as an intervention to address this issue. However, to date, there is little consideration of sex differences among caregivers in the design and planning of these interventions. OBJECTIVE: To systematically review the literature on technology-based interventions for caregivers of persons with dementia and report the frequency and approaches of sex-based analysis. METHODS: The literature was systematically searched for reviews of technology-based interventions for caregivers of persons with dementia. All titles and abstracts of publications included in the retrieved reviews were screened using pre-determined inclusion and exclusion criteria. Full text articles that met the inclusion criteria were included for analysis. RESULTS: < 0.05) between male and female caregivers. CONCLUSIONS: There is currently a lack of (1) sex-based analyses, (2) inclusion of males and (3) provision of sex-specific information in studies of technology-based interventions for caregivers of persons with dementia.
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 | 0.220 | 0.401 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.011 |
| Bibliometrics | 0.021 | 0.014 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".