The care capacity goals of family carers and the role of technology in achieving them
Bibliographic record
Abstract
BACKGROUND: As global populations age, governments have come to rely heavily on family carers (FCs) to care for older adults and reduce the demands made of formal health and social care systems. Under increasing pressure, sustainability of FC's unpaid care work has become a pressing issue. Using qualitative data, this paper explores FCs' care-related work goals, and describes how those goals do, or do not, link to technology. METHODS: We employed a sequential mixed-method approach using focus groups followed by an online survey about FCs' goals. We held 10 focus groups and recruited 25 FCs through a mix of convenience and snowball sampling strategies. Carer organizations helped us recruit 599 FCs from across Canada to complete an online survey. Participants' responses to an open-ended question in the survey were included in our qualitative analysis. An inductive approach was employed using qualitative thematic content analysis methods to examine and interpret the resulting data. We used NVIVO 12 software for data analysis. RESULTS: We identified two care quality improvement goals of FCs providing care to older adults: enhancing and safeguarding their caregiving capacity. To enhance their capacity to care, FCs sought: 1) foreknowledge about their care recipients' changing condition, and 2) improved navigation of existing support systems. To safeguard their own wellbeing, and so to preserve their capacity to care, FCs sought to develop coping strategies as well as opportunities for mentorship and socialization. CONCLUSIONS: We conclude that a paradigm shift is needed to reframe caregiving from a current deficit frame focused on failures and limitations (burden of care) towards a more empowering frame (sustainability and resiliency). The fact that FCs are seeking strategies to enhance and safeguard their capacities to provide care means they are approaching their unpaid care work from the perspective of resilience. Their goals and technology suggestions imply a shift from understanding care as a source of 'burden' towards a more 'resilient' and 'sustainable' model of caregiving. Our case study findings show that technology can assist in fostering this resiliency but that it may well be limited to the role of an intermediary that connects FCs to information, supports and peers.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 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".