What is ‘care quality’ and can it be improved by information and communication technology? A typology of family caregivers' perspectives
Bibliographic record
Abstract
INTRODUCTION: With governments worldwide reducing their involvement in the provision of institutional long-term elder care, community-based family caregivers (FCs) have become a key element in policies aimed at improving the quality of healthcare systems and maintaining their financial sustainability. This paper uses data from focus groups with FCs providing care to older adults to describe their approaches to and priorities for achieving care quality and sustainability as they work with formal health and social care systems. It describes FCs' views on information and communications technology (ICT) as potential supports for achieving these care quality and sustainability goals. METHODS: We held 10 focus groups from May 2017 to August 2018 and recruited 25 FCs through a mix of convenience and snowball sampling strategies. We employed an inductive approach and used qualitative thematic content analysis methods to examine and interpret the resulting data. We used NVIVO 12 software for data analysis. RESULTS: Quality of care - as delivered by both FCs themselves, and formal health and social care systems - was a major preoccupation for our participants. They saw communications quality as a key aspect of the broader concept of care quality. Our data analysis produced a typology of communications quality from the FC perspective. Analysis of our data also revealed ICT development opportunities and available products in key areas. CONCLUSIONS: Our findings suggest that the formal care system providers could be more caregiver-oriented in their communications by engaging FCs in the decision-making process and allowing them to express their own concerns and goals. The implication of our findings for those seeking to develop policies and ICT products in support of FCs is that these should focus on human relationships and seek to expand facilitative communications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".