Technology to Support Informal Caregivers: Matching the Tools to the Needs from a Sex and Gender Perspective
Bibliographic record
Abstract
Caregiving can be highly stressful and associated with poor mental and physical health. Technologies, including mobile and e-health applications, have been developed to address caregiver needs. Yet, although caregiving is a gendered activity, sex and gender considerations have not been incorporated systematically in understanding caregiving experiences and the design of these technologies. As such, this thesis aims to develop a tool to assist in the development of technology interventions to support caregiving by (1) synthesizing evidence on sex and gender distinctions in caregiving experiences and its physical and mental health impacts on informal caregivers, (2) examining caregiver’s knowledge, use, perceived usefulness and feature preferences of technology, and (3) exploring sex and gender influences on technology use and perceptions amongst informal caregivers. Findings from each objective are used to inform tool development. The thesis was based on three studies. The first study, a systematic review on sex and gender distinctions in caregiving experiences and its impact on informal caregivers’ physical and mental well-being, found 13 studies reporting higher caregiving burden among female caregivers. Results from the second study, a secondary analysis of a cross-sectional survey on technology needs and preferences of informal caregivers, revealed that most caregivers did not know much and had never used any technologies to assist with caregiving. Female respondents were more likely to have more knowledge about technology for caregiving while male respondents were more willing to pay higher amounts for these technologies. Findings from the third study involving semi-structured interviews with informal caregivers and technology researchers, highlighted the multi-faceted role technology can play in aiding caregiving, while at the same time draw attention to the pitfalls and drawbacks of these technologies perceived by caregivers. Together, the findings lead to the creation of the CareDATA (Caregiving Diversity and Technology Assessment) tool. It provides key considerations for incorporating sex, gender and diversity when developing technologies for caregiving. Overall, this thesis highlighted the complexities of sex, gender and diversity within the field of both caregiving experiences and technologies and represents a robust step towards the realization of more tailored technological solutions to support informal caregivers.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".